The DOI matches its Crossref record; 213 of the 278 references it cites were found in a registry or an index; none of the 100 sources its claims cite is checked for support yet.
Bringing AI to edge: From deep learning’s perspective
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
72 Not checked yet · 33 No basis given · 26 Own interpretation · 1 Own result
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Most of the above-mentioned structure pruning methods are rule-based, i.e., some heuristic algorithms devised according to their own criterion.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the probability of the correct class dominates the probability distribution generated by the teacher network (e.g., [0.97, 0.1, 0.0, 0.2]), which significantly limits the knowledge transferring capability.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Knowledge Distillation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“All of the above-mentioned approaches mainly search for the best operators for cells or layers where their width and depth are fine-tuned manually.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the majority of existing methods use platform-independent metrics, i.e., FLOPs and MACs as the constraint to design the model.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“as shown in [50], the width and depth of a DNN have a critical impact on its accuracy and latency.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
What the sources say
- Not checked yetcites [50] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“quantization becomes a promising approach to address the aforementioned issue, which encodes full-precision (FP32) weights and activations with low-precision ones (e.g., FP16, INT8, binary) while preserving even the same level of accuracy.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“searching in a discrete space using RL and EA is really costly”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
In the paper
“knowledge distillation can also be used to design robust networks [151].”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Knowledge Distillation
What the sources say
- Not checked yetcites [151] source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Knowledge distillation demonstrates better transferability for the small models, i.e., for a given dataset, learning the small model from knowledge distillation outperforms learning it from scratch.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Knowledge Distillation
What the sources say
- Not checked yetcites [150]
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“capable of compressing a DNN model, in some cases more than 40x, while only degrading the prediction accuracy slightly [76].”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
What the sources say
- Not checked yetcites [76]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“the gradient-based method is recently proposed to find an optimal pruning criterion for each layer [98].”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
What the sources say
- Not checked yetcites [98]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Most structure pruning methods adopt a uniform pruning criterion for all layers”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“these methods only consider the accuracy and ignore the performance like latency and power consumption, which are critical for edge intelligence systems.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, we notice that only two works evaluate their designs on edge settings (on ARM CPU, mobile GPU or low power accelerators)”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Object Detection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the above-mentioned works use indirect metrics like FLOPs or compression ratio to prune network.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“As estimated in [152], MnasNet needs 40,000 GPU hours to search for the optimal network.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
What the sources say
- Not checked yetcites [152]
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“the same model demonstrates significant difference on different hardware [170].”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
What the sources say
- Not checked yetcites [170] source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the existing NAS methods mainly target the models with high accuracy while ignoring other important system metrics, e.g., energy and latency.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“some pruning studies directly target the direct metric upon a specific hardware, e.g., latency.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“In contrary, some work employ learning algorithm to automatically prune network model.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Some early work has shown that using FP16 to train DNN models can reduce the computational cost while retaining accuracy [109].”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
What the sources say
- Not checked yetcites [109]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“since the weights and activations are quantized into discrete values, we cannot use stochastic gradient descent to update the weights.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“As identified in literature [149], knowledge distillation provides several benefits for small network models.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Knowledge Distillation
What the sources say
- Not checked yetcites [149]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“a larger T leads to a 'softer' probability distribution”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Knowledge Distillation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“many emergent hardware and accelerators support mixed precision operations, e.g., Nvidia Turing GPU architecture supports 1-bit, 4-bit, 8bit and 16-bit arithmetic operations [130].”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
What the sources say
- Not checked yetcites [130]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The RL-based or EA-based NAS frameworks explore the optimal architecture in a large, discrete search space, so they demand huge amount of computational resource and take thousands of GPU days [166] to search for a neural architecture.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
What the sources say
- Not checked yetcites [166] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Fig. 11 shows the latency of 100000 network architectures generated using DARTS [166] which are measured on Nvidia Jetson Xavier [203], where we can see that the models with the same latency may differ in the number of FLOPs by up to 26% (from 461 FLOPs to 623 FLOPs with latency 120ms)”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Different metrics oriented DNN models
What the sources say
- Not checked yetcites [166] source
- Not checked yetcites [203]
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“the models with the same FLOPs perform different latency ranging from below 80ms up to 120ms.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Different metrics oriented DNN models
What the sources say
- Not checked yetcites [166] source
- Not checked yetcites [203]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“These two models on Edge TPU also consume higher power (5W) than Inception V3 and ResNet50 (4.6W).”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Hardware-software co-design
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“However, static DNN models are unable to achieve such guarantee under dynamic environment [20].”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Motivation Behind This Paper
What the sources say
- Not checked yetcites [20] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Real-world edge deployments face rapidly changing conditions.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“one of these challenges is the computational gap between computation-intensive deep learning algorithms and less-capable edge systems.”
- Also stated in: § I. INTRODUCTION · plain
“This unavoidably leads to 'computational gap' between DNN models and less-capable edge systems.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § I. INTRODUCTION · plain
None of the sources cited for this claim have been checked yet.
In the paper
“but the high and uncertain communication overhead makes it difficult to satisfy the temporal requirement.”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
What the sources say
- Not checked yetcites [5]
- Not checked yetcites [11]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“proposes a compound scaling method such that given a DNN model and a target computational complexity (i.e., FLOPs) it can effectively adjust the three dimensions of a model to improve its predictive performance.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Classification
What the sources say
- Not checked yetcites [50] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“uploading these data to cloud may risk data leakage which will severely hurt their business.”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“To address these concerns, edge computing is proposed [12].”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
What the sources say
- Not checked yetcites [12] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the highly computational complexity of DNN models hinders them from being efficiently deployed on resource-constraint devices, e.g., edge or IoT devices.”
§ Bringing AI To Edge: From Deep Learning's Perspective > III. LIGHTWEIGHT NETWORK DESIGN
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The vast DNN design methods applicable to edge systems can be broadly classified into three categories: hand-crafted models, model compression and hardware-aware NAS.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Motivation Behind This Paper
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The research from Facebook shows the performance gain by porting DNN applications to specific hardware accelerators may not be able to compensate the significant engineering effort spent due to the poor ecosystems and program-ability [20].”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
What the sources say
- Not checked yetcites [20] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“but there is no review discussing all three categories which in future will be integrated seamlessly to design effective and efficient DNNs in the new edge intelligence era.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Motivation Behind This Paper
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“although some recent studies start to investigate how to design lightweight natural language processing (NLP) models for edge devices like, [33] [34] [35], they are far from mature like models and techniques for computer vision tasks.”
§ Bringing AI To Edge: From Deep Learning's Perspective > II. PRELIMINARIES
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to the computational gap, many edge intelligence systems cannot meet the expected performance requirements.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“All of these reviews only have a small fraction to discuss the edge DNN design which is the key element of edge intelligence systems.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Motivation Behind This Paper
What the sources say
- Not checked yetcites [13] source
- Not checked yetcites [23]
- Not checked yetcites [24]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is not difficult to see that it is unlikely to deploy these state-of-the-art models to edge devices, which have limited storage and computational resources.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It hinders users with limited resource to search for a DNN upon their target hardware devices.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The scaled models achieves a comparable or better accuracy while having fewer parameters.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Classification
What the sources say
- Not checked yetcites [50] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“We think this is becoming a mainstream trend to adopt NAS with novel lightweight operators to design edge DNN model.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Classification
What the sources say
- Not checked yetcites [53] source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Manual design of lightweight DNN models depends heavily on expert knowledge and requires lengthy hyperparameter exploration.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This leads to the searched model with low efficiency and high energy consumption.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consequently, the quantized models may suffer from significant accuracy loss.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“the huge memory requirement prohibits DNN models from implementing on memory-limited edge devices [127], [128].”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
What the sources say
- Not checked yetcites [127], [128]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“such designs may generate inefficient models for targeting hardware.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“FLOPs cannot necessarily reflect the runtime performance upon target hardware (see Fig. 11).”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“As indicated in [72], DNN models usually have significant redundancy in terms of weights and parameters.”
- Also stated in: § A. Network Pruning · plain
“The main motivation behind network pruning is that DNN models are usually over-parameterized in terms of weights and channels [72]”
§ Bringing AI To Edge: From Deep Learning's Perspective > IV. NETWORK COMPRESSION
What the sources say
- Not checked yetcites [72] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § A. Network Pruning · plain
In the paper
“Since different hardware architectures demonstrate different degrees of parallelism, the state-of-the-art methods may unnecessarily prune models without performance improvement (e.g., latency reduction) while reducing the capacity of DNN models which is proven to have a significant impact on model's accuracy [108].”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
What the sources say
- Not checked yetcites [108]
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Different hardware architectures demonstrate different degrees of parallelism.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“knowledge distillation facilitates training of a competitive, compact model with small dataset.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Knowledge Distillation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“as pointed in [49] using the indirect metrics lke FLOPs or MACs cannot directly translate to the relevant performance metric, i.e., latency and throughput.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Object Detection
What the sources say
- Not checked yetcites [49] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, existing methods overlook the impact of deployed hardware, on which some operators cannot be supported or effectively executed.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Execution variance of DNN applications require DNN models to be adaptive to the different input data and system status (like low power mode) for guaranteeing certain QoS or realtime performance.”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These together signal the importance of system-level hardware-software co-design.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Hardware-software co-design
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“edge DNN models are really application-dependent or context-dependent”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Different metrics oriented DNN models
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“for some lightweight models adaptive models can increase the capacity of models, thereby improving the accuracy of the model without increasing the computational cost (because it only needs to partially activates the network)”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“a group of edge systems is able to help each other to improve models by using federated learning [229].”
§ Bringing AI To Edge: From Deep Learning's Perspective > E. The data challenge at edge
What the sources say
- Not checked yetcites [229] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The existing adaptive models mainly consider the dynamics of input images, i.e., fewer channels or layers for 'easy' images and more channels or layers for 'complex' images.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methods
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“DNN models designed without hardware consideration may not fully utilize the underlying hardware.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Hardware-software co-design
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“By means of compressed GAN, some GAN-based applications, e.g., style transfer, image synthesis, etc, can be efficiently implemented on edge systems.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Lightweight models for other applications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“DNN models are found to be vulnerable to adversary attack [202]”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Different metrics oriented DNN models
What the sources say
- Not checked yetcites [202]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The partially activated network can achieve higher efficiency than the originally full network without accuracy loss.”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
What the sources say
- Not checked yetcites [172]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“This key observation serves the technical foundation for adaptive models, i.e., a network may selectively activate its channels and layers per input basis.”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
What the sources say
- Not checked yetcites [172]
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“this part is ignored in current research outcomes.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Although few efforts have been made towards efficient learning at the edge, such as [223], [224], learning at the edge is still at its early stage.”
§ Bringing AI To Edge: From Deep Learning's Perspective > D. Learning on The Edge
What the sources say
- Not checked yetcites [223]
- Not checked yetcites [224]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“These models, like BERT [217], are known to be highly complex, even more complicated than DNN models for image classification”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Lightweight models for other applications
What the sources say
- Not checked yetcites [217]
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Thus, the majority of edge intelligence systems without large-scale dataset exploit transfer learning [225] to learn a competitive model”
§ Bringing AI To Edge: From Deep Learning's Perspective > E. The data challenge at edge
What the sources say
- Not checked yetcites [225]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“as shown in [170], the same neural architecture demonstrates up to 62x difference on modern mobile devices in terms of inference time.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Hardware-software co-design
What the sources say
- Not checked yetcites [170] source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Some early works identify that for different input images, not all channels or layers of a DNN model are needed to make accurate prediction [172].”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
What the sources say
- Not checked yetcites [172]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“due to high communication overhead, on-time model update and possible leakage of confidential data, some edge systems prefer to train the model locally, i.e., on-device learning [222].”
§ Bringing AI To Edge: From Deep Learning's Perspective > D. Learning on The Edge
What the sources say
- Not checked yetcites [222] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“ResNet [7] is found to be tolerant to the removal of some blocks or layers without affecting the predictive accuracy [195].”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“this probably indicates that designing efficient DNN models will soon become a standard not only for edge systems but also all AI systems.”
§ Bringing AI To Edge: From Deep Learning's Perspective > VIII. CONCLUSION
What the sources say
- Not checked yetcites [40]
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the number of FLOPs may not directly translate to its latency, because DNN models have diverse architectures which may demonstrate different effectiveness on different hardware platforms.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Different metrics oriented DNN models
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Adaptive models provide a flexible way to achieve trade-off between accuracy and efficiency on the fly”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated learning is a promising solution to share the information among several models or data sources while still keeping the confidential of data.”
§ Bringing AI To Edge: From Deep Learning's Perspective > E. The data challenge at edge
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Some recent works study to compress the complex NLP models such that they can be deployed on resource-constrained edge systems [33] [34] [35].”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Lightweight models for other applications
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 38
- Probably not a substantial claim. 22
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“during the run-time, DNN applications share the computational units and communication bandwidth with other applications and as a result the availability of computing and communication resources may significantly affect DNNs' inference time.”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“ResNet-50 [7] has 26 million parameter weights, generates 16 million activations in one inference, requires around 168 MB memory space, and needs at lease 3GB/s memory bandwidth.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
What the sources say
- Not checked yetcites [7] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Almost all of pruning methods are hardware-agnostic and depend on hardware-independent metrics, like MACs and FLOPs.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Post-training quantization is flexible and can be broadly applied to existing DNN models.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“since operations with lowbit representation usually consume less energy and execute faster, quantization reduces energy consumption and latency as well on some hardware platforms [129].”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
What the sources say
- Not checked yetcites [129] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Knowledge distillation is another technique to conduct model compression, where a more compact student model can learn the knowledge from a complicated and powerful teacher model.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Knowledge Distillation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“a diverse of hardware accelerators have emerged for boosting the execution of DNNs, but these various systems demonstrate different capability to handling network complication.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“some edge systems are energy-constrained, battery powered or supplied by sustainable energy, like solar energy (intermittent computing [171]), and in these cases energy variance will change the system's status, such as scaling down frequency and power which affects the inference time of DNN applications as well.”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
What the sources say
- Not checked yetcites [171] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Model compression provides a 'large-to-small' method to generate an efficient and compact model from a complex model for edge devices.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Quantization has become a standard means to compress memory-hungry DNN models for resourceconstrained edge systems.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“neural architecture search, which has demonstrated its capability to design more accurate DNNs without tedious parameter tuning”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“distilling knowledge from large networks can improve the accuracy of small models which may be directly applicable to edge systems.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Knowledge Distillation
What the sources say
- Not checked yetcites [149]
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Pruning is the well-studied topic in model compression and many methods have been proposed to advance the network pruning [103].”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
What the sources say
- Not checked yetcites [103] source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“NAS defines a new paradigm to design DNN models, frees DNN designers from tedious hyperparameter tuning and automates the costly DNN design procedure.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the state-of-the-art models have more than billions of parameters and at the same time during inference a model produces a large portion of intermediate results (activation/feature maps) which usually occupy a large memory space.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“network compression involves a costly and time-consuming compression-retraining procedure to retain the accuracy of the pruned model.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“MnasNet and MobileNet V3 on Edge TPU perform very low latency, even lower than the high-end GPU”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Hardware-software co-design
What the sources say
- Not checked yetcites [53] source
- Not checked yetcites [45]
- Not checked yetcites [21]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, DNN models, the current core component of artificial intelligence, are highly computation-intensive and are even growing 'wider' (more filters within a layer) and 'deeper' (more layers) with billions of parameters and millions of float point operations (FLOPs)”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“we also discuss adaptive DNN models which are also not reviewed systematically in previous articles.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Motivation Behind This Paper
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“However, developing chips is a costly process, 30-80 million dollars and 2-3 years to develop [19].”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
What the sources say
- Not checked yetcites [19]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Depth-wise separable convolution can significantly reduce the computational complexity, thus shortening inference time on edge devices.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Classification
What the sources say
- Not checked yetcites [43]
- Not checked yetcites [46]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Convolutional layers account for the major resource and time cost of a CNN model.”
§ Bringing AI To Edge: From Deep Learning's Perspective > II. PRELIMINARIES
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“SqueezeNet achieves AlexNet-level accuracy while greatly reducing the model complexity.”
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Classification
What the sources say
- Not checked yetcites [41]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Different methodologies are devised to approach this goal, like novel light-weight DNN model design, model compression, and the emergent neural architecture search (NAS) [22]”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
What the sources say
- Not checked yetcites [22]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“However, inference from cloud suffers from responsiveness (e.g., latency) and privacy issues which are critical in some scenarios.”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
What the sources say
- Not checked yetcites [5]
- Not checked yetcites [11]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“some customized edge accelerators achieve high efficiency at the cost of generality, e.g., edge TPU only supports a limited number of operators [21] and some new DNN architectures cannot be wellsupported.”
§ Bringing AI To Edge: From Deep Learning's Perspective > I. INTRODUCTION
What the sources say
- Not checked yetcites [21]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“less-capable edge systems”
- Also stated in: § I. INTRODUCTION · plain
“resource-constrained edge systems”
§ Bringing AI To Edge: From Deep Learning's Perspective > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § I. INTRODUCTION · plain
None of the sources cited for this claim have been checked yet.
In the paper
“The off-the-shelf hardware and software cannot efficiently execute those compressed formats, so specialized hardware and software libraries are required to execute sparsified DNN models [83], [84].”
§ Bringing AI To Edge: From Deep Learning's Perspective > Filters Channels Filters Channels
What the sources say
- Not checked yetcites [83]
- Not checked yetcites [84] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the accuracy of the pruned model is inherently upper bounded by the pre-trained model, i.e., the compressed model cannot have better accuracy than the pre-trained model.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since onestage methods are less computational than two-stage methods, all of these methods mentioned above target one-stage method for improving efficiency of object detection DNNs.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Object Detection
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Experimental results show deep compression can significantly reduce the model size with no or negligible accuracy loss.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Kernel Weights
What the sources say
- Not checked yetcites [75] source
- Not checked yetcites [76]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“it is capable of fitting the whole model into on-chip memory of edge devices such that the high overhead occurred by off-chip memory access can be mitigated.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Quantization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The one-stage approaches can trade off accuracy for efficiency, but the practical deployment on edge devices is still behind the expected performance [70].”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Object Detection
What the sources say
- Not checked yetcites [70] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Deep Compression uses three steps, prunning, quantization, and Huffman Encoding, to compress over-parameterized DNN models, such as VGG [77], AlexNet [1].”
§ Bringing AI To Edge: From Deep Learning's Perspective > Kernel Weights
What the sources say
- Not checked yetcites [75] source
- Not checked yetcites [76]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The two-stages methods provide high accuracy at the cost of efficiency, and even on powerful GPUs they cannot guarantee real-time constraints (e.g., 25fps for video).”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Object Detection
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is worthy noting that the three approaches are not mutual exclusive to each other and in many cases they are combined to maximally compress the redundant models.”
§ Bringing AI To Edge: From Deep Learning's Perspective > IV. NETWORK COMPRESSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since structured pruning does not lead to irregular pattern, the compressed network pruned by structure pruning can directly accelerate its inference on off-theshelf hardware platforms without specialized software library support.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Filters Channels Filters Channels
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“different layers have different functions, thereby likely benefiting from employing different pruning criteria at different layers.”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Different layers have different functions.
None of the sources cited for this claim have been checked yet.
In the paper
“edge systems can update models locally by using incremental learning [226] [227], where techniques in [228] are deployed to improve the accuracy for new data and infer unknown classes.”
§ Bringing AI To Edge: From Deep Learning's Perspective > E. The data challenge at edge
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It is known that accuracy of DNN models are highly related to its width (channels or feature maps) and depth (layers or blocks) [50]”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Hardware-software co-design
What the sources say
- Not checked yetcites [50] source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As stated in [40], DL algorithms are approaching the computational limits of computing systems”
§ Bringing AI To Edge: From Deep Learning's Perspective > VIII. CONCLUSION
What the sources say
- Not checked yetcites [40]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Such variance may influence the quality of service (QoS) of applications without rigorously temporal requirement, like voice recognition, face recognition, machine translation, etc [20] and on the other hand may lead to catastrophic consequence for those with rigorously temporal requirement, (i.e., real-time requirement), like autonomous driving, UAV, etc.”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
What the sources say
- Not checked yetcites [20] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“FNAS can achieve the same accuracy level under different resource constraints, while significantly reducing the search cost and inference latency.”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Hardware-software co-design
What the sources say
- Not checked yetcites [204]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The academia and industry have a consensus that the breakthroughs in hardware architecture will bring DNNs to a new level and boost DNN adoption [15] [205].”
§ Bringing AI To Edge: From Deep Learning's Perspective > B. Hardware-software co-design
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Attention technique mimics human visual systems to locate their focal point on the important area and up-weights the areas of interest to improve the model accuracy.”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
What the sources say
- Not checked yetcites [174] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Like [218], an efficient point-voxel CNN is proposed for efficient 3D learning, and this model can help to implement 3D AR/VR applications and SLAM [219] of autonomous driving on edge systems.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Lightweight models for other applications
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“a recent study in [9] raises a concern regarding the environment effect of training complex NLP models.”
§ Bringing AI To Edge: From Deep Learning's Perspective > C. Lightweight models for other applications
What the sources say
- Not checked yetcites [9] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“instead of sharing the data with a centralized server clients (e.g., an edge system) in FL only share the learned gradient with others which will not leak data.”
§ Bringing AI To Edge: From Deep Learning's Perspective > E. The data challenge at edge
What the sources say
- Not checked yetcites [229] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Recently, few works study to employ incremental learning [227] and federated learning [230] [231] with edge systems, but the research in this context is still in its infancy.”
§ Bringing AI To Edge: From Deep Learning's Perspective > E. The data challenge at edge
What the sources say
- Not checked yetcites [227]
- Not checked yetcites [230]
- Not checked yetcites [231]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Attention has shown its successful application in CV [174] and NLP [180].”
§ Bringing AI To Edge: From Deep Learning's Perspective > VI. ADAPTIVE MODELS
What the sources say
- Not checked yetcites [174] source
- Not checked yetcites [180]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, on edge systems, some system dynamics, memory contention, cache miss, bandwidth unavailability, etc, also impact the execution of DNN application”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“data privacy is one motivation of edge systems, so it may be impossible to collect data from different edge systems and share them with each other.”
§ Bringing AI To Edge: From Deep Learning's Perspective > E. The data challenge at edge
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper relies on this without support.
At present, edge intelligence systems are used mainly for computer vision tasks.
“currently computer vision models are the major application for edge intelligence systems”
The paper relies on this without support.
Real-world edge deployments face rapidly changing conditions.
“Edge systems installed in real world operate in highly dynamic environment”
The paper relies on this without support.
Manual design of lightweight DNN models depends heavily on expert knowledge and requires lengthy hyperparameter exploration.
“The manual design of lightweight DNN models is considerably dependent on experts' knowledge and also needs a time-consuming hyperparameter exploration.”
The paper relies on this without support.
Different layers have different functions.
“different layers have different functions”
The paper relies on this without support.
Reductions in FLOPs and compression ratio do not directly translate into performance improvement.
“the reduced FLOPs and compression ratio cannot directly translate to the performance improvement.”
The paper relies on this without support.
Different hardware architectures demonstrate different degrees of parallelism.
“different hardware architectures demonstrate different degrees of parallelism”
The paper relies on this without support.
The design space for DNN is too large for exhaustive search.
“In addition, design space for DNN is too large to have an exhaustive search.”
The paper relies on this without support.
There is a lack of measurement of trade-offs between metrics, leading to high search cost or sub-optimal design.
“However, we still lack the measurement of the trade-off between each metric and this will lead to either high searching cost or sub-optimal design result.”
The paper relies on this without support.
Different hardware accelerators have diverse underlying characteristics, and most DNNs were designed without considering underlying hardware features.
“However, different hardware accelerators features diverse underlying characteristics, and the majority of DNNs were designed without consideration of underlying hardware features.”
The paper relies on this without support.
The majority of works on deep learning for edge systems target computer vision tasks such as image classification and object detection.
“Currently, the majority of works regarding deep learning on edge systems target computer vision tasks, i.e., image classification and object detection.”
The paper relies on this without support.
On-device learning is challenging because training is more computation-intensive and memory-hungry than inference, while edge systems are resource-constrained.
“Ondevice learning is a challenging task, because training is a more computation-intensive and memory-hungry procedure compared to inference, whereas edge systems are resourceconstrained in many settings.”
The paper relies on this without support.
Limited energy supply of some edge systems makes on-device learning even more difficult.
“Moreover, limited energy supply of some edge systems will make this issue even more difficult.”
The paper relies on this without support.
The success of DNN models heavily relies on high-quality and large-scale datasets.
“The success of DNN models heavily relies on high-quality and large-scale datasets, such as ImageNet”
The paper relies on this without support.
For some edge applications, collecting and labeling massive data is difficult or expensive.
“for some edge applications, e.g., edge surveillance systems in wild life, defect detection in manufacturing process, etc, it is difficult or expensive to collect massive amount of data and label them to train a good model.”
The paper relies on this without support.
During long-term operation, edge systems are likely to collect data with different distributions or new classes, requiring model updates.
“during the long-term operation, edge systems are likely to collect data with different distributions from the original training data or data pertaining to a new class which is not included in the original training data, we may need to update the model on edge systems”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Machine learning frameworks: ML frameworks, such as TensorFlow Lite and PyTorch, are used to develop and run machine learning models on edge devices [202].”
From Edge AI: A survey (2023) · cites this paper as [202]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
“HERE, COST REFERS TO THE TIME SPENT TO SEARCH THE NETWORK, WHILE WE USE GPU Hour AS UNIT.”
§ Bringing AI To Edge: From Deep Learning's Perspective > V. HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH
“Cost (GPU days) Metrics Target Edge Systems MnasNet [53] Measure RL 40000”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Bringing AI To Edge: From Deep Learning's Perspective > A. Different metrics oriented DNN models
“Fig. 11 shows the latency of 100000 network architectures generated using DARTS [166]”
§ Bringing AI To Edge: From Deep Learning's Perspective > VIII. CONCLUSION
“Fig. 11.Latency vs FLOPs: The latency measurements of 10000 different architectures with different FLOPs count.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
“BinaryConnect [111] 1-bit Yes No 2016”
§ Bringing AI To Edge: From Deep Learning's Perspective > References
“111. M. Courbariaux, Y. Bengio, and J.-P. David, "Binaryconnect: Training deep neural networks with binary weights during propagations," in Advances in neural information processing systems, 2015, pp. 3123- 3131.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Bringing AI To Edge: From Deep Learning's Perspective > Methods
“BranchyNet [189] Early-exit Gradient 2017”
§ Bringing AI To Edge: From Deep Learning's Perspective > References
“189. S. Teerapittayanon, B. McDanel, and H.-T. Kung, "Branchynet: Fast inference via early exiting from deep neural networks," in 2016 23rd International Conference on Pattern Recognition (ICPR).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
“Li et al. [87] Inference-based Layer Rule-based 2016”
§ Bringing AI To Edge: From Deep Learning's Perspective > Methodologies
“Li et al. [87] adopt a global pruning method”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 3×
“For instance, training ResNet50 [7] with ImageNet on 8 Nvidia Tesla P100 takes 29 hours [8], where ImageNet dataset [2] has 1.28 M images of 1000 classes for training.”
BackgroundMethodInfluentialVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 1×
“Deep Compression uses three steps, prunning , quantization , and Huffman Encoding , to compress over-parameterized DNN models, such as VGG [77], AlexNet [1].”
MethodBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 2×
“For instance, training ResNet50 [7] with ImageNet on 8 Nvidia Tesla P100 takes 29 hours [8], where ImageNet dataset [2] has 1.28 M images of 1000 classes for training.”
BackgroundMethodInfluentialBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325 · cited 1×
“These models, like BERT [217], are known to be highly complex, even more complicated than DNN models for image classi-fication, and a recent study in [9] raises a concern regarding the environment effect of training complex NLP models.”
MethodYou Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · cited 3×
“To boost the efficiency of object detection on resource-limited devices, some works strive to reduce the computational complexity of backbone part of object detection models, like Tiny-dsod [58] for DSOD [69], Tiny-SSD [59] for SSD [65], and Tiny-YOLO [57] for YOLO.”
MethodA fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · cited 1×
“EXPERIMENTAL RESULTS ARE UPON TWO WIDELY-USED BENCHMARK, PASCAL VOC2007 [54] AND MS COCO [55].”
MethodSqueeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · cited 1×
“To improve the performance and training speed, modern DNN models also have other operation layers, like batch normalization layer [38], squeeze-and-excitation layer [39], etc.”
MethodRich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · cited 1×
“RCNN [66], Fast RCNN [67], and 3 https://https://www.automl.org/ Faster RCNN [68] are representative examples of two-stage methods.”
Method
Research works printed but not linked: 37
- A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: P.L. Bartlett, F.C.N. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, 2012, pp. 1106–1114. URL:https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
- Goodfellow, 2016, vol. 1
- Ieee computer society’s top 12 technology trends for 2020, URL: https://www.computer.org/press-room/2019-news/ieee-computer-societys-top-12-technology-trends-for-2020, access: 2020-5-14.
- Wei, 2021, Wireless edge caching based on content similarity in dynamic environments, Journal of Systems Architecture, 102000
- Choudhary, 2020, A comprehensive survey on model compression and acceleration, Artificial Intelligence Review, 1
- Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, M. Bennamoun, Deep learning for 3d point clouds: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence.
- M. Feurer, A. Klein, K. Eggensperger, J.T. Springenberg, M. Blum, F. Hutter, Efficient and robust automated machine learning, in: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7–12, 2015, Montreal, Quebec, Canada, 2015, pp. 2962–2970. URL:https://proceedings.neurips.cc/paper/2015/hash/11d0e6287202fced83f79975ec59a3a6-Abstract.html.
- J. Wang, T.A. Bohn, C.X. Ling, Pelee: A real-time object detection system on mobile devices, in: S. Bengio, H.M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3–8, 2018, Montréal, Canada, 2018, pp. 1967–1976. URL:https://proceedings.neurips.cc/paper/2018/hash/9908279ebbf1f9b250ba689db6a0222b-Abstract.html.
- S. Ren, K. He, R.B. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks, in: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7–12, 2015, Montreal, Quebec, Canada, 2015, pp. 91–99. URL:https://proceedings.neurips.cc/paper/2015/hash/14bfa6bb14875e45bba028a21ed38046-Abstract.html.
- M. Denil, B. Shakibi, L. Dinh, M. Ranzato, N. de Freitas, Predicting parameters in deep learning, in: C.J.C. Burges, L. Bottou, Z. Ghahramani, K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5–8, 2013, Lake Tahoe, Nevada, United States, 2013, pp. 2148–2156. URL: https://proceedings.neurips.cc/paper/2013/hash/7fec306d1e665bc9c748b5d2b99a6e97-Abstract.html.
- Y. LeCun, J.S. Denker, S.A. Solla, Optimal brain damage, in: Advances in neural information processing systems, 1990, pp. 598–605.
- S. Han, J. Pool, J. Tran, W.J. Dally, Learning both weights and connections for efficient neural network, in: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7–12, 2015, Montreal, Quebec, Canada, 2015, pp. 1135–1143. URL: https://proceedings.neurips.cc/paper/2015/hash/ae0eb3eed39d2bcef4622b2499a05fe6-Abstract.html.
- S. Han, H. Mao, W.J. Dally, Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, International Conference on Learning Representations (ICLR).
- D. Molchanov, A. Ashukha, D.P. Vetrov, Variational dropout sparsifies deep neural networks, in: D. Precup, Y.W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017, Vol. 70 of Proceedings of Machine Learning Research, PMLR, 2017, pp. 2498–2507. URL: http://proceedings.mlr.press/v70/molchanov17a.html.
- Deb, 2001, vol. 16
- H. Li, A. Kadav, I. Durdanovic, H. Samet, H.P. Graf, Pruning filters for efficient convnets, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24–26, 2017, Conference Track Proceedings, OpenReview.net, 2017. URL: https://openreview.net/forum?id=rJqFGTslg.
- W. Wen, C. Wu, Y. Wang, Y. Chen, H. Li, Learning structured sparsity in deep neural networks, in: D.D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, R. Garnett (Eds.), Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5–10, 2016, Barcelona, Spain, 2016, pp. 2074–2082. URL: https://proceedings.neurips.cc/paper/2016/hash/41bfd20a38bb1b0bec75acf0845530a7-Abstract.html.
- M. Courbariaux, Y. Bengio, J. David, Binaryconnect: Training deep neural networks with binary weights during propagations, in: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7–12, 2015, Montreal, Quebec, Canada, 2015, pp. 3123–3131. URL: https://proceedings.neurips.cc/paper/2015/hash/3e15cc11f979ed25912dff5b0669f2cd-Abstract.html.
- A. Tarvainen, H. Valpola, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, in: I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S.V.N. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, 2017, pp. 1195–1204. URL: https://proceedings.neurips.cc/paper/2017/hash/68053af2923e00204c3ca7c6a3150cf7-Abstract.html.
- H. Cai, C. Gan, T. Wang, Z. Zhang, S. Han, Once-for-all: Train one network and specialize it for efficient deployment, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26–30, 2020, OpenReview.net, 2020. URL: https://openreview.net/forum?id=HylxE1HKwS.
- C. Li, Z. Yu, Y. Fu, Y. Zhang, Y. Zhao, H. You, Q. Yu, Y. Wang, C. Hao, Y. Lin, HW-nas-bench: Hardware-aware neural architecture search benchmark, in: International Conference on Learning Representations, 2021. URL: https://openreview.net/forum?id=_0kaDkv3dVf.
- J. Lin, Y. Rao, J. Lu, J. Zhou, Runtime neural pruning, in: I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S.V.N. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, 2017, pp. 2181–2191. URL: https://proceedings.neurips.cc/paper/2017/hash/a51fb975227d6640e4fe47854476d133-Abstract.html.
- B.E. Bejnordi, T. Blankevoort, M. Welling, Batch-shaping for learning conditional channel gated networks, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26–30, 2020, OpenReview.net, 2020. URL: https://openreview.net/forum?id=Bke89JBtvB.
- T. Bolukbasi, J. Wang, O. Dekel, V. Saligrama, Adaptive neural networks for efficient inference, in: D. Precup, Y.W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017, Vol. 70 of Proceedings of Machine Learning Research, PMLR, 2017, pp. 527–536. URL: http://proceedings.mlr.press/v70/bolukbasi17a.html.
- K. Xu, J. Ba, R. Kiros, K. Cho, A.C. Courville, R. Salakhutdinov, R.S. Zemel, Y. Bengio, Show, attend and tell: Neural image caption generation with visual attention, in: F.R. Bach, D.M. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6–11 July 2015, Vol. 37 of JMLR Workshop and Conference Proceedings, JMLR.org, 2015, pp. 2048–2057. URL: http://proceedings.mlr.press/v37/xuc15.html.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need, in: I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S.V.N. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, 2017, pp. 5998–6008. URL: https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html.
- A. Veit, M.J. Wilber, S.J. Belongie, Residual networks behave like ensembles of relatively shallow networks, in: D.D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, R. Garnett (Eds.), Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5–10, 2016, Barcelona, Spain, 2016, pp. 550–558. URL: https://proceedings.neurips.cc/paper/2016/hash/37bc2f75bf1bcfe8450a1a41c200364c-Abstract.html.
- T. Chen, T. Moreau, Z. Jiang, L. Zheng, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. Ceze, et al., {TVM}) An automated end-to-end optimizing compiler for deep learning, in: 13th {USENIX})Symposium on Operating Systems Design and Implementation ({OSDI})18), 2018, pp. 578–594.
- Z. Liu, H. Tang, Y. Lin, S. Han, Point-voxel CNN for efficient 3d deep learning, in: H.M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E.B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8–14, 2019, Vancouver, BC, Canada, 2019, pp. 963–973. URL: https://proceedings.neurips.cc/paper/2019/hash/5737034557ef5b8c02c0e46513b98f90-Abstract.html.
- I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A.C. Courville, Y. Bengio, Generative adversarial nets, in: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8–13 2014, Montreal, Quebec, Canada, 2014, pp. 2672–2680. URL: https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html.
- Y. Wang, Z. Jiang, X. Chen, P. Xu, Y. Zhao, Y. Lin, Z. Wang, E2-train: Training state-of-the-art cnns with over 80% energy savings, in: H.M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E.B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8–14, 2019, Vancouver, BC, Canada, 2019, pp. 5139–5151. URL: https://proceedings.neurips.cc/paper/2019/hash/663772ea088360f95bac3dc7ffb841be-Abstract.html.
- Pan, 2009, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, 22, 1345, 10.1109/TKDE.2009.191 · DOI · Paper page
- Dosovitskiy, 2021, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, International Conference on Learning Representations (ICLR)
- Luo, 2021, HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search, Design, Automation \& Test in Europe Conference \& Exhibition (DATE)
- Luo, 2021, Designing Efficient DNNs via Hardware-Aware Neural Architecture Search and Beyond, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)
- Kong, 2022, HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs, Asia and South Pacific Design Automation Conference (ASP-DAC)
- Huai, 2021, ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems, ACM/IEEE Design Automation Conference (DAC)
Cited by
Sorted by citations
Singh, R., & Gill, S. S. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.02.004 · Cited by 572
Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 439
“There have been some surveys investigating AI-powered cloud-edge orchestration [5], [6], [8], [9], [10], edge intelligence [12], [13], [14], [15], end-edge-cloud orchestration [7], [11], AI/ML for wireless edge network [16], [17], and privacy and security for distributed learning [18].”
BackgroundEdge AI: A Taxonomy, Systematic Review and Future Directions
Gill, S. S., Golec, M., Hu, J., Xu, M., Du, J., Wu, H., Walia, G. K., Murugesan, S. S., Ali, B., Kumar, M., et al. (2024) · Cluster Computing · doi:10.1007/s10586-024-04686-y · Cited by 280
“The study emphasises the significance and changing challenges of resource management [8], whereas a presentation framework is described that addresses the issue of sparse training data in emerging radio access networks by utilizing a range of methods, such as interpolation, domain-knowledge…”
BackgroundEdge deep learning in computer vision and medical diagnostics: a comprehensive survey
Xu, Y., Khan, T. M., Song, Y., & Meijering, E. H. W. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11033-5 · Cited by 187
E-YOLO: Recognition of estrus cow based on improved YOLOv8n model
Wang, Z., Hua, Z., Wen, Y., Zhang, S., Xu, X., & Song, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122212 · Cited by 128
Metadata
- Authors
- Liu, D., Kong, H., Luo, X., Liu, W., & Subramaniam, R.
- Affiliations
- Nanyang Technological University (SG)
- Hewlett-Packard (United States) (US)
- Corresponding author
- Luo, Xiangzhong
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 485
- Pages
- 297-320
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3110617657
- Cited by
- 171
- References
- 278
- Citations by year
- 2026: 17 · 2025: 51 · 2024: 40 · 2023: 31 · 2022: 24
- FWCI
- 8.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neurocomputing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nanyang Technological University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- edge AI, edge computing, deep learning, lightweight models, model compression, neural architecture search, hardware-aware neural architecture search, adaptive deep learning, edge devices
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
