The DOI matches its Crossref record; 140 of the 168 references it cites were found in a registry or an index; 10 of the 54 sources its claims cite were checked for support.
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
Open accessAbstract
With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.
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10 of 54 cited sources checked · 4 supported · 0 flagged · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Stamoulis et al. [106] cast the adaptive DNN model selection issue as a hyperparameter optimization problem by taking into account the accuracy and communication constraints imposed by the devices.Then, a Bayesian optimization (BO) is adopted to solve this problem, achieving an improvement by up to 6× in terms of minimum energy per image under accuracy constraints.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > Park et al.
What the sources say
- Supportedcites [106] 106. D. Stamoulis et al., "Designing adaptive neural networks for energy-constrained image classification," in Proc. ACM ICCAD, 2018, Art. no. 23. source
Section 4.1 (p. 4) formulates adaptive CNN design, including network selection, as a hyperparameter optimization problem. Section 4.2 (p. 4) says the authors adapt Bayesian optimization to solve it, and the Conclusion (p. 8) reports up to 6× lower minimum energy per image under accuracy constraints than previously published methods. (LLM, conf=0.94)
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The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The result varies with architectures and transfer techniques.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [62] 62. R. Sharma, S. Biookaghazadeh, B. Li, and M. Zhao, "Are existing knowledge transfer techniques effective for deep learning with edge devices?" in Proc. IEEE Int. Conf. Edge Comput. (EDGE), Jul. 2018, pp. 42-49. source
- Could not verifycites [44] 44. Q. Chen, Z. Zheng, C. Hu, D. Wang, and F. Liu, "Data-driven task allocation for multi-task transfer learning on the edge," in Proc. IEEE 39th Int. Conf. Distrib. Comput. Syst. (ICDCS), 2019. sourceExperimental — accuracy not yet measured
Section 5.2.2 (p. 6) reports accuracy for one architecture under different distributed-training conditions, but does not compare architectures or transfer-learning techniques. Section 6 (p. 6) calls studies of other DNN architectures future work. Searches for transfer learning and variation across architectures or transfer techniques found no passage addressing the claim. (LLM, conf=0.76)
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In the paper
“Cachier can increase responsiveness by 3× or more.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) Edge Caching:
What the sources say
- Could not verifycites [93] 93. U. Drolia, K. Guo, J. Tan, R. Gandhi, and P. Narasimhan, "Cachier: Edge-caching for recognition applications," in Proc. IEEE ICDCS, Jun. 2017, pp. 276-286. sourceExperimental — accuracy not yet measured
The Results section discusses Edgent’s inference latency, but does not report a result for Cachier. Searches for “Cachier” and “responsiveness three times” found no passage supporting a 3× improvement. (LLM, conf=0.77)
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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 Generated at the Network Edge Need AI to Fully Unlock Their Potential”
- Also stated in: § A. Motivation and Benefits of Edge Intelligence · boosted
“AI will be functionally necessary due to its ability to quickly analyze those huge data volumes and extract insights from them for high-quality decision making.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > A. Motivation and Benefits of Edge Intelligence
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § A. Motivation and Benefits of Edge Intelligence · boosted
In the paper
“Cisco estimates that nearly 850 ZB will be generated by all people, machines, and things at the network edge by 2021 [3].”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > I. I N T R O D U C T I O N
What the sources say
- Not matchedcites [3] 3. Cisco Global Cloud Index: Forecast and Methodology, 2016-2021, White Paper. [Online]. Available: https://www.cisco.com/c/en/us/ solutions/collateral/service-provider/global- cloud-index-gci/white-paper-c11-738085.html
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“Lin et al. [53] observe that 99.9% of the gradient exchange in distributed SGD (DSGD) are redundant, which demonstrates the power of gradient compression.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 3) Gradient Compression:
What the sources say
- Not checked — no full text of the sourcecites [53] 53. Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally, "Deep gradient compression: Reducing the communication bandwidth for distributed training," 2017, arXiv:1712.01887. [Online]. Available: https://arxiv.org/abs/1712.01887 source
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Specifically, while preserving client's own privacy, the training loss can be reduced by sharing the models among clients, comparing to training solely on their own inputs.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
What the sources say
- Not checked — no full text of the sourcecites [46] 46. R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," in Proc. 22nd ACM SIGSAC Conf. Comput. Commun. Secur., 2015, pp. 1310-1321. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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 Computing Is Able to Prosper AI With Richer Data and Application Scenarios”
- Also stated in: § 2) Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios: · hedged
“edge computing is proposed to achieve low-latency data processing by sinking the computing capability from the cloud data center to the edge side, i.e., data generation source, which may enable AI processing with high performance.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios:
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 2) Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios: · hedged
In the paper
“A flaw of [46] is that it does not consider unbalanced and nonindependent identical distribution (non-IID) data.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
What the sources say
- Not checked — no full text of the sourcecites [46] 46. R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," in Proc. 22nd ACM SIGSAC Conf. Comput. Commun. Secur., 2015, pp. 1310-1321. source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“big data have recently gone through a radical shift of data source from the megascale cloud datacenters to the increasingly widespread end devices, e.g., mobile devices and Internetof-Things (IoT) devices.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > I. I N T R O D U C T I O N
What the sources say
- Not matchedcites [3] 3. Cisco Global Cloud Index: Forecast and Methodology, 2016-2021, White Paper. [Online]. Available: https://www.cisco.com/c/en/us/ solutions/collateral/service-provider/global- cloud-index-gci/white-paper-c11-738085.html
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
- Probably not a substantial claim. 28
- It may state two separate claims as one. 27
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“VGG-16 [19] is trained to a deeper structure of 16 layers consisting of 13 convolution layers and three fully connected layers, requiring 138 million weights and 15.5G MACs to classify the image with a size of 224 × 224.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Convolution Neural Network:
What the sources say
- Partly supportedcites [19] 19. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," 2014, arXiv:1409.1556. [Online]. Available: https://arxiv.org/abs/1409.1556 sourceExperimental — accuracy not yet measured
Table 1 (p. 3) identifies configuration D as a 16-weight-layer network with a 224 × 224 RGB input, and Table 2 (p. 3) lists 138 million parameters for D. Section 2.1 (p. 2) describes the three fully connected layers. Our copy does not report the claimed 15.5G MACs per image, so that part is unsupported. (LLM, conf=0.88)
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- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“In particular, an update aggregation protocol named FedCS is developed to allow the centralized server to aggregate as many client updates as possible and to accelerate performance improvement in ML models.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Aggregation Frequency Control:
What the sources say
- Could not verifycites [52] 52. T. Nishio and R. Yonetani, "Client selection for federated learning with heterogeneous resources in mobile edge," 2018, arXiv:1804.08333. [Online]. Available: https://arxiv.org/abs/ 1804.08333 sourceExperimental — accuracy not yet measured
The Introduction (p. 2) describes pruning, quantization, and Huffman coding, while §6.3 (pp. 9–10) discusses inference speedups from compression. Searches for FedCS, client-update aggregation, and federated client selection found no passage addressing the claimed protocol or its effect on model improvement. (LLM, conf=0.76)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“The optimization in [45] emphasizes the properties of unbalanced and non-IID since the distributed data may come from various sources.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
What the sources say
- Supportedcites [45] 45. H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, "Communication-efficient learning of deep networks from decentralized data," 2016, arXiv:1602.05629. [Online]. Available: https://arxiv.org/abs/1602.05629 source
The Introduction (p. 2) explicitly says the work emphasizes the non-IID and unbalanced properties of federated optimization. It explains that clients’ data reflect different users’ device usage and that users contribute varying amounts of data, supporting the citing sentence’s explanation of why those properties arise. (LLM, conf=0.94)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“when moving a tremendous amount of data across the wide area network (WAN), both monetary cost and transmission delay can be prohibitively high, and the privacy leakage can also be a major concern [5].”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > I. I N T R O D U C T I O N
What the sources say
- Could not verifycites [5] 5. Q. Pu et al., "Low latency geo-distributed data analytics," in Proc. ACM SIGCOMM, 2015, pp. 421-434. sourceExperimental — accuracy not yet measured
The Introduction and Section 6 (Advantages and disadvantages; page numbers unavailable) discuss generative models and their computational properties, not transferring large datasets over a WAN. Searches for WAN costs, transmission delay, and privacy leakage found no passage addressing the claim. (LLM, conf=0.75)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Could not verify
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“the physical proximity between the computing and information-generation sources promises several benefits compared to the traditional cloud-based computing paradigm, including low latency, energy efficiency, privacy protection, reduced bandwidth consumption, on-premises, and context awareness [6], [8].”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > I. I N T R O D U C T I O N
What the sources say
- Not checked — no full text of the sourcecites [6] 6. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet Things J., vol. 3, no. 5, pp. 637-646, Oct. 2016. source
- Supportedcites [8] 8. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A survey on mobile edge computing: The communication perspective," IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322-2358, 4th Quart., 2017. source
The comparison in “Mobile Edge Computing Versus Mobile Cloud Computing” (p. 3) identifies lower latency, mobile-device energy savings, context awareness, and improved privacy and security as MEC advantages. “Mobile Computing for 5G: From Clouds to Edges” (p. 1) explains that moving traffic toward the edge avoids burdening backhaul networks, while the privacy discussion (p. 3) gives enterprise-managed deployment as an example of keeping data on premises. (LLM, conf=0.88)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“A good performance improvement is obtained by transferring knowledge from both the intermediate layers and last layer of the teacher to a shallower student while other architectures and transfer techniques do not fare so well and some of them even lead to negative performance impact.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [62] 62. R. Sharma, S. Biookaghazadeh, B. Li, and M. Zhao, "Are existing knowledge transfer techniques effective for deep learning with edge devices?" in Proc. IEEE Int. Conf. Edge Comput. (EDGE), Jul. 2018, pp. 42-49. source
- Could not verifycites [44] 44. Q. Chen, Z. Zheng, C. Hu, D. Wang, and F. Liu, "Data-driven task allocation for multi-task transfer learning on the edge," in Proc. IEEE 39th Int. Conf. Distrib. Comput. Syst. (ICDCS), 2019. sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
Every source the paper cites for this claim was checked and supports it.
In the paper
“The early work on random gossip algorithms is gossip averaging [63], which can fast converge toward a consensus among nodes by exchanging information peer to peer.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Supportedcites [63] 63. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, "Randomized gossip algorithms," IEEE Trans. Inf. Theory, vol. 52, no. 6, pp. 2508-2530, Jun. 2006. source
The paper describes gossip averaging as nodes communicating with randomly chosen neighbors (Randomized Gossip Algorithms, p. 1). It studies convergence to the vector of averages and the design of the fastest gossip algorithm (Previous Results, p. 3; Randomized Gossip Algorithms, p. 1), supporting the peer-to-peer consensus paraphrase. (LLM, conf=0.89)
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- Supported
In the paper
“The algorithm is based on the convergence analysis of distributed gradient descent and can be applied to federated learning in edge computing with provable convergence.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Aggregation Frequency Control:
What the sources say
- Not matchedcites [51] 51. S. Wang et al., "Adaptive federated learning in resource constrained edge computing systems," IEEE J. Sel. Areas Commun., vol. 37, no. 3, pp. 1205-1221, Jun. 2019.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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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
“As a result, the transmission latency of data offloading decreases, the data privacy increases, and the WAN bandwidth cost reduces.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 5) Level 4-Cloud-Edge Cotraining and Inference: Train-
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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 main challenges of federated learning are optimization and communication.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“While edge computing and AI complement each other from a technical perspective, their application and popularization are also mutually beneficial.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios:
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. 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
“Though the federated learning technique exploits a new decentralized deep learning architecture, it is built upon a central server for aggregating local updates.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
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In the paper
“As forecast by Gartner [33], more than 80% of enterprise IoT projects will include an AI component by 2022, up from only 10% today.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > A. Motivation and Benefits of Edge Intelligence
What the sources say
- Not matchedcites [33] 33. AI Trends for Enterprise Computing. [Online]. Available: https://www.gartner.com/ smarterwithgartner/3-ai-trends-for-enterprise- computing/
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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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 conflict indicates that there is no”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 5) Level 4-Cloud-Edge Cotraining and Inference: Train-
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“Energy efficiency is mainly affected by the size of the target training model and resources of the used devices.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 6) Energy Efficiency:
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
“ResNet [20], the state-of-the-art effort, uses the”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Convolution Neural Network:
What the sources say
- Not checked — no full text of the sourcecites [20] 20. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE CVPR, Jun. 2016, pp. 770-778. source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“real-time video analytics is envisioned to be a killer application for edge computing [12], [37], [38].”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) Edge Computing Can Be Popularized With AI Applications:
What the sources say
- Not checked — no full text of the sourcecites [12] 12. G. Ananthanarayanan et al., "Real-time video analytics: The killer app for edge computing," Computer, vol. 50, no. 10, pp. 58-67, 2017. source
- Not checked — no full text of the sourcecites [37] 37. H. Zhang, G. Ananthanarayanan, P. Bodik, M. Philipose, P. Bahl, and M. J. Freedman, "Live video analytics at scale with approximation and delay-tolerance," in Proc. USENIX NSDI, 2017, pp. 377-392.
- Not checked — no full text of the sourcecites [38] 38. C.-C. Hung et al., "VideoEdge: Processing camera streams using hierarchical clusters," in Proc. IEEE/ACM Symp. Edge Comput. (SEC), Oct. 2018, pp. 115-131. source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AlexNet requires 61 million weights and 724 million MACs (multiply-add computation) to classify the image with a size of 227 × 227.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Convolution Neural Network:
What the sources say
- Not matchedcites [18] 18. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proc. NIPS, 2012, pp. 1097-1105.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“However, Gaia focuses on geodistributed datacenters that are capacity-unlimited, making it is not generally applicable to edge computing nodes whose capacity is highly constrained.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Aggregation Frequency Control:
What the sources say
- Not matchedcites [50] 50. K. Hsieh, A. Harlap, N. Vijaykumar, D. Konomis, G. R. Ganger, and P. B. Gibbons, "Gaia: Geo-distributed machine learning approaching LAN speeds," in Proc. NSDI, 2017, pp. 629-647.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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- Not matched
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
“Under the decentralized training mode, the convergence value depends on the way the gradient is synchronized and updated.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Convergence:
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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
“When training the DNN model by using the data originated at a massive of end devices, the raw data or intermediate data should be transferred out of the end devices.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 3) Privacy:
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. 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
“Deep learning represents the state-of-the-art AI technology as well as a highly resource-demanding workload that naturally suits for edge computing.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > B. Deep Learning and Deep Neural Networks
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt 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
“When training the DNN model in a decentralized manner, both the computation and communication processes consume enormous energy.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 6) Energy Efficiency:
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: For most end devices, they are energy-constrained.
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, this is achieved at the cost of increased computational latency and energy consumption.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 5) Level 4-Cloud-Edge Cotraining and Inference: Train-
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“GoogleNet achieves a better accuracy performance than VGG-16, while only requiring seven million weights and 1.43G MACs to process the image with the same size.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Convolution Neural Network:
What the sources say
- Not checked — no full text of the sourcecites [28] 28. C. Szegedy et al., "Going deeper with convolutions," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 1-9. source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“MeDNN improves the DNN model inference by 1.86-2.44×with two to four worker nodes and saves 26.5% of additional computing time and 14.2% of extra communication time.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Model Compression:
What the sources say
- Not checked — no full text of the sourcecites [84] 84. J. Mao et al., "MeDNN: A distributed mobile system with enhanced partition and deployment for large-scale DNNs," in Proc. 36th Int. Conf. Comput.-Aided Design, Nov. 2017, pp. 751-756. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Communication overhead is affected by the size of the original input data, the way of transmission, and the available bandwidth.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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 combinatorial nature, the high-dimensional configuration problem involves a huge search space of parameters and is very challenging to tackle.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > D. Summary of Existing Systems and Frameworks
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Standard performance indicators such as top-k accuracy or mean average precision fail to reflect the runtime performance of DNN model inference on edge devices.
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, data parallelism may bring heavy overhead of communication, while model parallelism usually leads to severe underutilization of computation resources.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
In the paper
“PipeDream shows the advantage of reducing communication overhead and utilizing computing resource efficiently.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [61] 61. A. Harlap, D. Narayanan, A. Phanishayee, V. Seshadri, G. R. Ganger, and P. B. Gibbons, "PipeDream: Fast and efficient pipeline parallel DNN training," 2018, arXiv:1806.03377. [Online]. Available: https://arxiv.org/abs/1806.03377 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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 communication latency may vary from the size of transmitted raw or intermediate data and the bandwidth of network connection.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“By deployment on large-scale systems, Daily et al. [66] show that the trivial gossip-based algorithms at scale lead to a communication imbalance, poor convergence, and heavy communication overhead.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [66] 66. J. Daily, A. Vishnu, C. Siegel, T. Warfel, and V. Amatya, "GossipGraD: Scalable deep learning using gossip communication based asynchronous gradient descent," 2018, arXiv:1803.05880. [Online]. Available: https://arxiv.org/ abs/1803.05880 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“FoggyCache reduces computation latency and energy consumption by a factor of 3× to 10×.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) Edge Caching:
What the sources say
- Not checked — no full text of the sourcecites [95] 95. P. Guo, B. Hu, R. Li, and W. Hu, "FoggyCache: Cross-device approximate computation reuse," in Proc. ACM Mobicom, 2018, pp. 19-34. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
“Arguably, latency is one of the most fundamental performance indicators of distributed DNN model training since it directly influences when the trained model is available for use.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“This is based on the important observation that a DNN model can be split inside between two successive layers with two partitions deployed on different locations without losing accuracy.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“the proposed automatic optimization framework AdaDeep [75] systematically formulates the goals and constraints on accuracy, latency, storage, and energy into a unified optimization problem and leverages DRL to effectively find a good combination of compression techniques.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Model Compression:
What the sources say
- Not checked — no full text of the sourcecites [75] 75. S. Liu, Y. Lin, Z. Zhou, K. Nan, H. Liu, and J. Du, "On-demand deep model compression for mobile devices: A usage-driven model selection framework," in Proc. 16th Annu. Int. Conf. Mobile Syst., Appl., Services, 2018, pp. 389-400. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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 decentralized architecture is naturally friendly to user's privacy, for which the systems that are based on the decentralized architecture such as BlockFL and GossipGraD typically preserve privacy better.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > Table 2 Overview of Systems and Frameworks on EI Model Training
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
“Intuitively, this communication overhead increases the training latency, energy, and bandwidth consumption.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Precog uses predictions of Markov chains to prefetch data onto the mobile device and reach a speedup of 5×.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) Edge Caching:
What the sources say
- Not checked — no full text of the sourcecites [94] 94. U. Drolia, K. Guo, and P . Narasimhan, "Precog: Prefetching for image recognition applications at the edge," in Proc. ACM/IEEE Symp. Edge Comput. (SEC), Oct. 2017, p. 17. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Compared with the DNN training under the cloud-based framework, the DNN training under edge-based framework pays more attention to protecting users' privacy and training an available deep learning model faster.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > Table 2 Overview of Systems and Frameworks on EI Model Training
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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 latency of the distributed training process typically consists of the computation latency and the communication latency.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Both Blot et al. [64] and Jin et al. [65] apply gossip algorithms on the updates of SGD, but neither of them has performance convergence degradation at large scale.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [64] 64. M. Blot, D. Picard, M. Cord, and N. Thome, "Gossip training for deep learning," 2016, arXiv:1611.09726. [Online]. Available: https://arxiv.org/abs/1611.09726 source
- Not checked — no full text of the sourcecites [65] 65. P. H. Jin, Q. Yuan, F. Iandola, and K. Keutzer, "How to scale distributed deep learning?" 2016, arXiv:1611.04581. [Online]. Available: https://arxiv.org/abs/1611.04581 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“ReXCam reduces computation workload by 4.6× and improves DNN model inference accuracy by 27%.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 5) Input Filtering:
What the sources say
- Not checked — no full text of the sourcecites [99] 99. S. Jain, J. Jiang, Y. Shu, G. Ananthanarayanan, and J. Gonzalez, "ReXCam: Resource-efficient, cross-camera video analytics at enterprise scale," 2018, arXiv:1811.01268. [Online]. Available: https://arxiv.org/abs/1811.01268 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For the object recognition application, Huang et al. [120] investigate the influence of the main factors, e.g., number of proposals, input image size, and the selection of feature extractor, on inference speed and accuracy.Based on their experiment results, a new combination of these factors is found to outperform the state-of-the-art method.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > D. Tradeoff Design With Various DNN Performance Metrics
What the sources say
- Not checked — no full text of the sourcecites [120] 120. J. Huang et al., "Speed/accuracy trade-offs for modern convolutional object detectors," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 7310-7311. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Standard performance indicators such as top-k accuracy or mean average precision fail to reflect the runtime performance of DNN model inference on edge devices.
In the paper
“Compared to the approach that uploads the entire model, IONN significantly improves query performance and energy consumption during DNN model uploading.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Model Compression:
What the sources say
- Not checked — no full text of the sourcecites [82] 82. H.-J. Jeong, H.-J. Lee, C. H. Shin, and S.-M. Moon, "IONN: Incremental offloading of neural network computations from mobile devices to edge servers," in Proc. ACM Symp. Cloud Comput., 2018, pp. 401-411. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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 gossip distributed algorithms enjoy the advantage of full asynchronization and total decentralization as they have no requirement on centralized nodes or variables.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“the cascading network [91] simply adds the MP layer and fully connected layer to the standard DNN model and achieves a speedup of 20%.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > The aggregation methods work when multiple mobile
What the sources say
- Not checked — no full text of the sourcecites [91] 91. S. Leroux et al., "The cascading neural network: Building the Internet of smart things," Knowl. Inf. Syst., vol. 52, no. 3, pp. 791-814, 2017.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“This process will tend to work if the features are general (i.e., suitable to both base and target tasks) instead of specific to the base task.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper relies on this without support.
There is a lack of a dedicated venue for summarizing, discussing, and disseminating recent advances of edge intelligence in both industry and academia.
“There is, in general, a lack of venue dedicated for summarizing, discussing, and disseminating the recent advances of EI, in both industrial and academia.”
The paper relies on this without support.
Research and practice on edge intelligence are still in a very early stage.
“Notably, research and practice on this emerging interdiscipline-EI-are still in a very early stage.”
The paper relies on this without support.
The edge ecosystem will present many novel application scenarios for AI and fuel the continuous booming of AI.
“Clearly, via bringing the huge volumes of data to AI, the edge ecosystem will present many novel application scenarios for AI and fuel the continuous booming of AI.”
The paper relies on this without support.
For most end devices, they are energy-constrained.
“However, for most end devices, they are energy-constrained.”
The paper relies on this without support.
It is inevitable to deal with privacy issues when raw or intermediate data are transferred out of end devices.
“Obviously, it is inevitable to deal with privacy issues in this scenario.”
The paper relies on this without support.
For some real-time intelligent mobile applications, they usually have stringent deadline requirement such as 100-ms latency.
“For some real-time intelligent mobile applications (e.g., AR/VR mobile gaming and intelligent robots), they usually have stringent deadline requirement such as 100-ms latency.”
The paper relies on this without support.
For some mobile applications requiring high reliability, they demand ultrahigh accuracy on DNN model inference.
“For some mobile applications requiring a high level of reliability, such as self-driving car and face authentication, they demand the ultrahigh accuracy on the DNN model inference.”
The paper relies on this without support.
Most existing efforts use a single compression technique.
“While most existing efforts use a single compression technique, they may not suffice to meet the diverse requirements and constraints on accuracy, latency, storage, and energy imposed by some IoT devices.”
The paper relies on this without support.
Standard performance indicators such as top-k accuracy or mean average precision fail to reflect the runtime performance of DNN model inference on edge devices.
“However, it is difficult for software developers to choose an appropriate DNN model for the EI application because the standard performance indicators such as top-k accuracy or mean average precision fail to reflect the runtime performance of DNN model inference on edge devices.”
The paper relies on this without support.
Most current edge AI programming/software platforms mainly serve as relays for connecting to cloud data centers.
“Nevertheless, currently, most of these platforms mainly serve as relays for connecting to the powerful cloud data centers.”
The paper relies on this without support.
Most deep learning-based AI models are highly resource-intensive.
“Most of the deep learning-based AI models are highly resourceintensive, which means that powerful computing capability supported by abundant hardware resources (e.g., GPU, field-programmable gate array (FPGA), and TPU) is important to boost the performance of these AI models.”
The paper relies on this without support.
For an EI application with a specific mission, there is usually a series of DNN model candidates that are capable of finishing the task.
“For an EI application with a specific mission, there is usually a series of DNN model candidates that are capable of finishing the task.”
The paper relies on this without support.
The service environments for many EI application scenarios are of high dynamics and it is hard to accurately predict future events.
“Since for many EI application scenarios (e.g., smart cities), the service environments are of high dynamics and it is hard to accurately predict future events.”
The paper relies on this without support.
The high-efficiency operation of EI services may require close collaboration and integration across different service providers.
“The high-efficiency operation of EI services may require close collaboration and integration across different service providers, e.g., for implementing expanded resource sharing and smooth service handover.”
The paper relies on this without support.
A user can be a service consumer and, meanwhile, a data generator as well.
“Also, for EI service, a user can be a service consumer and, meanwhile, a data generator as well.”
The paper relies on this without support.
There is a stringent need to push the AI frontier from the cloud to the network edge.
“Driving by flourishing of both AI and IoT, there is a stringent need to pushing the AI frontier from the cloud to the network edge.”
The paper relies on this without support.
Edge computing is widely recognized as a promising solution to support computation-intensive AI applications in resource-constrained environments.
“To fulfill this trend, edge computing has been widely recognized as a promising solution to support computation-intensive AI applications in resource-constrained environments.”
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
“Additionally, UAVs generate lots of data at the network edge and directly sharing original data sets between many edge nodes has a high risk associated with it [8].”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [8]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Edge Intelligence (EI) Zhou et al. (2019) proposes a new paradigm combining AI and edge computing, with the goal of performing distributed computing of DL models.”
From Deepfakes: current and future trends (2024) · cites this paper as Zhou et al. 2019
The cited source says what the claim attributes to it.
In the citing paper
“This entails some advantages such as low latency, energy and bandwidth efficiency, privacy protection, and context awareness (Zhou et al. 2019).”
From Deepfakes: current and future trends (2024) · cites this paper as Zhou et al. 2019
The cited source says what the claim attributes to it.
In the citing paper
“In the case of training a deep learning model in an edge computing environment, where distributed models are trained locally first and then updates are aggregated centrally (e.g., federated learning), the control of updates aggregation frequency has a significant influence on the communication overhead.”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [8]
In the citing paper
“Edge AI provides a promising solution for connected intelligence by enabling data collection, processing, transmission, and consumption at the network edge [29], [30].”
From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [30]
In the citing 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.”
From Bringing ai to edge: from deep learning’s perspective (2022) · cites this paper as [13]
In the citing paper
“Smart city [113] is another important application scenario for deep-learning-enabled edge computing.”
From Deep learning for edge computing applications: A State-of-the-Art survey (2020) · cites this paper as [113]
In the citing paper
“In the current scenario, Blockchain provides a high degree of privacy for health data [49].”
From 6G Communication Technology: A Vision on Intelligent Healthcare (2021) · cites this paper as [49]
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.
Inference metrics are counted as five but six are listed
MinorThe section introduces five inference metrics, but its numbered list runs from latency through memory footprint and contains six.
“To describe the service quality of the EI model inference, we introduce the following five metrics.”
“## 6) Memory Footprint:”
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
Very 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 5×
“GoogleNet achieves a better accuracy performance than VGG-16, while only requiring seven million weights and 1.43G MACs to process the image with the same size.”
BackgroundMethodInfluentialImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 7×
“This is because for DNNs as exemplified by AlexNet, the energy of the convolutional layers dominates the total energy cost, while the number in the fully connected layers contributes most of the total number of weights in the DNN.”
BackgroundMethodInfluentialHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 4×
“4, each neuron in LSTM is called memory cell and includes a multiplicative forget gate, input gate, and output gate.”
BackgroundInfluentialLeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
You 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
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · cited 2×
“GoogleNet achieves a better accuracy performance than VGG-16, while only requiring seven million weights and 1.43G MACs to process the image with the same size.”
MethodLearning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · cited 1×
“Leveraging the backpropagation mechanism [26], [27], the error rate is propagated back across the whole neural network, and the weights are updated based on the gradient and the learning rate.”
(2016) · Cited by 31,590
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · cited 2×
“[32] V.”
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Research works printed but not linked: 11
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- hsieh, 2017, Gaia: Geo-distributed machine learning approaching LAN speeds, Proc NSDI, 629
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- 2018, 5 Trends Emerge in the Gartner Hype Cycle for Emerging Technologies
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Sorted by citations
Items 701–800 of 2,413
Dong, Z. Y., & Wang, T. (2024) · Energy internet. · doi:10.1049/ein2.12010 · Cited by 19
Li, Z., Chen, Z., Wei, X., Gao, S. B., Yue, H., Xu, Z., & Quek, T. Q. S. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3449129 · Cited by 18
The Road Toward General Edge Intelligence: Standing on the Shoulders of Foundation Models
He, L., Fan, L., Lei, X., et al. (2025) · IEEE Communications Magazine · doi:10.1109/mcom.003.2400445 · Cited by 16
“Edge intelligence (EI) [4] optimizes the use of data and resources across end devices, edge nodes, and It involves collaboration between cloud, edge, and device via data offloading.”
InfluentialSuzaki, K., Tsukamoto, A., Green, A., & Mannan, M. (2020) · Annual Computer Security Applications Conference · doi:10.1145/3427228.3427293 · Cited by 17
“The AI-Edge IoT Devices are managedbyEdge-Computing[24,87,92,110]orFog-Computing[69, 83],whichdecouplescomputingresourcesintoacloudandIoT devices,andmaintainsmanyIoTdevicesbyaremoteserverau-tomaticallythroughamachine-to-machine(M2M)management system.”
BackgroundMethodEDISON: An Edge-Native Method and Architecture for Distributed Interpolation
Lovén, L., Lähderanta, T., Ruha, L., et al. (2021) · Sensors · doi:10.3390/s21072279 · Cited by 16
EdgeLD: Locally Distributed Deep Learning Inference on Edge Device Clusters
Feng, X., Fang, W. W., Xu, W., Wang, Q., Ma, X., & Ding, Y. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00078 · Cited by 20
“Such resource demands become prohibitive for edge devices, e.g., embedded boards and smartphones, due to limitations in processing capacity and memory space [1].”
BackgroundInfluentialYoon, G., Kim, G.-Y., Yoo, H., Kim, S., & Kim, R. (2021) · IEEE Access · doi:10.1109/access.2021.3118731 · Cited by 16
“In the edge computing paradigm, the computing capability is located from the network core to the network edge that is in close proximity to the mobile edge devices and is capable of providing low-latency object detection services [8].”
BackgroundEdge Learning for Low-Latency Video Analytics: Query Scheduling and Resource Allocation
Lin, J., Yang, P., Wu, W. R., Zhang, N., Han, T., & Yu, L. (2021) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass52906.2021.00041 · Cited by 16
Memory optimization at Edge for Distributed Convolution Neural Network
Naveen, S., & Kounte, M. R. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4648 · Cited by 17
“(2) Memory requirement to deploy the model and process the data.”
BackgroundAdaptive resource optimization for edge inference with goal-oriented communications
Binucci, F., Banelli, P., Lorenzo, P. D., & Barbarossa, S. (2022) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-022-00958-0 · Cited by 19
“The need to tightly control latency and limit energy consumption motivates the shift toward edge intelligence (EI) architectures [3], where the information exchange and processing are kept as local as possible.”
BackgroundZhang, N., Tao, M., Wang, J., et al. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3224977 · Cited by 16
“This is exacerbated in the cases of the federated learning paradigm [2], [3] and the cloud-edge AI systems [9], where the wireless channel bandwidth between edge server and edge devices is limited.”
BackgroundLiu, Z., Conti, A., Mitter, S. K., et al. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3242731 · Cited by 16
“In distributed learning, the sensing, communication, and computing capabilities of different nodes in a network are exploited for learning unknown states with no or minimum involvement of a central processor [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19].”
BackgroundEdge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge
Zobaed, S., Mokhtari, A., Champati, J. P., Kourouma, M., & Salehi, M. A. (2022) · 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc56403.2022.00012 · Cited by 15
“1) The Scope for Edge Intelligence: Numerous research have been undertaken to explore the applications, scopes, and benefits of edge-based AI for the seamless execution of latency-sensitive smart applications [11], [12], [2], [13].”
BackgroundAlahmari, S., & Alghamdi, I. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107849 · Cited by 21
Makina, H., & Ben Letaifa, A. (2023) · International Journal of Communication Systems · doi:10.1002/dac.5484 · Cited by 16
“Zhou et al.(43) categorized the Edge intelligence into 6 levels based on the amount and path length of data offloading.”
BackgroundLightweight Federated Learning Over Wireless Edge Networks
Hou, X., Wang, J., Du, J., Jiang, C., Ren, Y., & Niyato, D. T. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3594102 · Cited by 15
Zhao, C., Liu, G., Zhang, R., Liu, Y., Wang, J., Kang, J., Niyato, D. T., Li, Z., Shen, X., Han, Z., et al. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3658762 · Cited by 31
“Edge computing and edge intelligence have emerged as a compelling response to the limitations of cloud-only approaches, including high latency, bandwidth costs, privacy concerns, and scalability bottlenecks [4], [5], [6], [7].”
Bilal, K., Shuja, J., Erbad, A. M., et al. (2022) · Sensors · doi:10.3390/s22031092 · Cited by 15
“ML techniques in general, and EI specifically, are called for the identification of multicast video patterns in a service-less 5G multicast architecture [13,14].”
MethodTowards automatic model specialization for edge video analytics
Rivas, D., Guim, F., Polo, J., Silva, P. M., Berral, J. L., & Carrera, D. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.03.039 · Cited by 14
“Video analytics, powered by all kinds of DNNs (Deep Neural Networks), is already one of the prominent use cases being executed at the edge [2].”
BackgroundCCEI-IoT: Clustered and Cohesive Edge Intelligence in Internet of Things
Dehury, C., Donta, P. K., Dustdar, S., & Srirama, S. (2022) · International Conference on Edge Computing [Services Society] · doi:10.1109/edge55608.2022.00017 · Cited by 16
Joint Detection and Computation Offloading With Age of Information in Mobile Edge Networks
Feng, J., & Gong, J. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3208857 · Cited by 15
“size limitations, mobile and IoT devices are generally computation-constrained, making it difficult to meet the quality-ofservice (QoS) requirements [1], [3].”
BackgroundMulti-User Goal-Oriented Communications With Energy-Efficient Edge Resource Management
Binucci, F., Banelli, P., Lorenzo, P. D., et al. (2023) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2023.3275199 · Cited by 15
“This has led to the definition of the so called Edge Intelligence (EI) paradigm [2].”
BackgroundEnergy-Efficient Dynamic Asynchronous Federated Learning in Mobile Edge Computing Networks
Xu, G., Li, X., Li, H., Fan, Q., Wang, X., & Leung, V. C. M. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278887 · Cited by 15
“1) Evaluation Settings: In simulations, we mainly consider the energy consumption of MDs. Similar to[18], the related parameters are set as follows: transmission bandwidth b i and computing ability f i are randomly distributed in [5 , 10] MHz and [2 , 8] GHz, respectively.”
BackgroundMethodEdge-AI Empowered Dynamic VNF Splitting in O-RAN Slicing: A Federated DRL Approach
Amiri, E., Wang, N., Shojafar, M., et al. (2023) · IEEE Communications Letters · doi:10.1109/lcomm.2023.3341302 · Cited by 15
“Edge AI plays a crucial role in expediting decision-making for dynamic network management [3], [4].”
BackgroundEdge Intelligence Optimization for Large Language Model Inference with Batching and Quantization
Zhang, X.-Y., Liu, J., Xiong, Z.-H., Huang, Y., Xie, G.-C., & Zhang, R. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10571127 · Cited by 22
“The authors in [4] delineated the advantages of edge intelligence, while also highlighting the challenges of executing efficient inference on resource-constrained edge nodes.”
BackgroundAfachao, K., Abu-Mahfouz, A. M. I., & Hancke, G. P. (2024) · IEEE Access · doi:10.1109/access.2024.3461149 · Cited by 15
Memory-Latency-Accuracy Trade-Offs for Continual Learning on a RISC-V Extreme-Edge Node
Ravaglia, L., Rusci, M., Capotondi, A., Conti, F., Pellegrini, L., Lomonaco, V., Maltoni, D., & Luca Benini, C. (2020) · SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems · doi:10.1109/sips50750.2020.9195220 · Cited by 15
Gu, X., Zhang, G., & Cao, Y. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4095 · Cited by 15
“Finally, Zhou et al 14 suggested that pushing the AI frontiers to MEC, is a promising solution to fully unleash the potential of the edge big data, and they reviewed the emerging efforts on AI model toward training/inference at the network edge.”
BackgroundCHA: A Caching Framework for Home-based Voice Assistant Systems
Xu, L., Iyengar, A., & Shi, W. (2020) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec50012.2020.00041 · Cited by 14
“Many efforts have been devoted to enabling edge intelligence [12], [13], in both hardware (e.”
BackgroundImpact of Federated Learning On Smart Buildings
Mitra, A., Ngoko, Y., & Trystram, D. (2021) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais50930.2021.9395938 · Cited by 18
“A 6 level rating for edge intelligence [9] generalization error as shown in Fig.”
BackgroundMethodIntegrated 3C in NOMA-Enabled Remote-E-Health Systems
Liu, X., Liu, Y., Yang, Z., et al. (2021) · IEEE Wireless Communications · doi:10.1109/mwc.001.2000403 · Cited by 15
“To fulfill these requirements, EI is proposed to push network functions from the network centre to the network edge [8].”
BackgroundIntelligent Sensing Scheduling for Mobile Target Tracking Wireless Sensor Networks
Zhou, L., Leng, S., Liu, Q., Chai, H., & Zhou, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3125530 · Cited by 16
“In addition, the limited energy of sensor nodes cannot assure consecutive target tracking when the mobile targets invade with high moving speed [6], [7].”
BackgroundJoint compressing and partitioning of CNNs for fast edge-cloud collaborative intelligence for IoT
Zhang, W., Wang, N., Li, L., & Wei, T. (2022) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2022.102461 · Cited by 14
Artificial intelligence in edge devices
Singh, A., Saini, K., Nagar, V., Aseri, V., Sankhla, M. S., Pandit, P. P., & Chopade, R. L. (2022) · Advances in computers · doi:10.1016/bs.adcom.2022.02.013 · Cited by 17
Nguyen, T. V., An, J., Oh, S., Truong, S. N., & Min, K. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac781a · Cited by 15
“…one promising candidate for implementing energy-efficient and low-precision AI hardware including edge-intelligence (Keshavarzi and van den Hoek 2019, Krestinskaya et al 2019, Zhou et al 2019, Deng et al 2020, Keshavarzi et al 2020, Ran et al 2020, Xue et al 2020, Qin et al 2020a, Singh et al 2021).”
BackgroundCloud computing and 5G challenges and open issues
Ullah, A., Aznaoui, H., Şahin, C. B., Rafie, M., Dinler, Ö. B., & Laassar, I. (2022) · International Journal of Advances in Applied Sciences · doi:10.11591/ijaas.v11.i3.pp187-193 · Cited by 15
Personalized federated learning for heterogeneous data: A distributed edge clustering approach
Firdaus, M., Noh, S., Qian, Z., Larasati, H. T., & Rhee, K.-H. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023475 · Cited by 16
“Leading companies such as Google, Microsoft, Intel, and IBM have initiated pilot projects to showcase the benefits of edge computing in the last mile of AI [19].”
BackgroundAccelerated Computer Vision Inference with AI on the Edge
Mittal, V., & Bhushan, B. (2020) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt48778.2020.9115770 · Cited by 14
“This gave birth to “AI on the Edge” [12, 13].”
Data Partition and Rate Control for Learning and Energy Efficient Edge Intelligence
Li, X.-Y., Wang, S., Zhu, G., Zhou, Z., Huang, K.-B., & Gong, Y. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3173262 · Cited by 13
Self-citationAdaptive and Collaborative Edge Inference in Task Stream with Latency Constraint
Song, J., Liu, Z., Wang, X., Qiu, C., & Chen, X. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500892 · Cited by 15
Self-citationGradient Statistics Aware Power Control for Over-the-Air Federated LearningPreprint
Zhang, N., & Tao, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.02089 · Cited by 15
6G Communications: A Vision on the Potential ApplicationsPreprint
Nayak, S., & Patgiri, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07531 · Cited by 15
“Edge Intelligence is combining Edge computing and AI [27].”
Wireless for Machine LearningPreprint
Hellström, H., da Silva, J. M. D., Fodor, V., & Fischione, C. (2020) · arXiv.org · doi:10.48550/arxiv.2008.13492 · Cited by 19
“2019 Proceedings of the IEEE [33] Classifies the main network architectures of Edge AI and discusses enabling technologies such as FL and other ML algorithmic.”
BackgroundEfficient Gesture-Based Recognition of Tifinagh Characters in Air Handwriting with a TinyDL Model
Lamaakal, I., El Mourabit, N., El Makkaoui, K., Ouahbi, I., & Maleh, Y. (2024) · 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds62089.2024.10756483 · Cited by 14
Al-shamasneh, A. R. M., Karim, F. K., & Wang, Y. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106387 · Cited by 17
Kalpani, N., & Rodrigo, N. (2025) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-025-00264-0 · Cited by 16
“[8] further discuss the potential of edge computing to offload detection tasks from centralised servers, thus reducing latency and bandwidth consumption, but also highlight the need for efficient, scalable algorithms that can run on edge devices.”
Feasibility Analysis of Machine Learning Optimization on GPU-based Low-cost Edges
Suo, J., Zhang, X., Zhang, S., Zhou, W., & Shi, W. (2021) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/swc50871.2021.00022 · Cited by 16
Counteract Side-Channel Analysis of Neural Networks by Shuffling
Brosch, M., Probst, M., & Sigl, G. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774710 · Cited by 14
Edge Intelligence with Distributed Processing of DNNs: A Survey
Tang, S., Cui, M., Qi, L., & Xu, X. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.023684 · Cited by 15
Efficient Distributed DNNs in the Mobile-Edge-Cloud Continuum
Malandrino, F., Chiasserini, C. F., & di Giacomo, G. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3222640 · Cited by 15
“Other works envision similar approaches, based on choosing the right network node to run each layer of a DNN and accounting for device capability [28], network latency [28], [29], and privacy [28], [30].”
MethodContainer‐based data‐intensive application scheduling in hybrid cloud‐edge collaborative environment
Tang, B., Luo, J., & Zhang, J. (2023) · Software Practice and Experience · doi:10.1002/spe.3195 · Cited by 14
Self-citationTchaye-Kondi, J., Zhai, Y., Shen, J., & Zhu, L. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3266257 · Cited by 14
“2a, the AE takes advantage of DNN splitting [25] property and splits the network architecture into two parts: Encoder and Decoder.”
RoaD-RuNNer: Collaborative DNN partitioning and offloading on heterogeneous edge systems
Kakolyris, A. K., Katsaragakis, M., Masouros, D., et al. (2023) · Design, Automation, and Test in Europe · doi:10.23919/date56975.2023.10137279 · Cited by 14
“Research conducted by [11] considers various present and future challenges for efficiently deploying deep learning models on the edge.”
BackgroundTrustworthy decentralized collaborative learning for edge intelligence: A survey
Yu, D., Xie, Z., Yuan, Y., Chen, S., Qiao, J., Wang, Y., Yu, Y. H., Zou, Y., & Zhang, X. L. (2023) · High-Confidence Computing · doi:10.1016/j.hcc.2023.100150 · Cited by 17
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization
Long, Q., Anagnostopoulos, C., Parambath, S., & Bi, D.-N. (2023) · Industrial Conference on Data Mining · doi:10.1109/icdm58522.2023.00146 · Cited by 18
“But in general, methods that can produce compact models along with significant redundancy in the number of DNN weights by sophisticatedly pruning the weights are deemed appropriate [13].”
MethodMulticore Spiking Neuromorphic Chip in 180-nm With ReRAM Synapses and Digital Neurons
Jiang, H., Lu, J., Zhang, C., Tang, S., An, J., Cheng, L., Lü, J., Wei, J., Zhou, K., Zhang, X., et al. (2023) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2023.3325158 · Cited by 14
“The corresponding practical application of artificial intelligence requires processing a large amount of data, necessitating computing hardware with great requirements on computing speed and energy efficiency [3].”
BackgroundZhang, Z., Zhao, Y., & Liu, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48424-7_18 · Cited by 16
Edge Intelligence for IoT Services in 6G Integrated Terrestrial and Non-Terrestrial Networks
Liu, Q., Wang, S., Qi, Z., et al. (2024) · IEEE Network · doi:10.1109/mnet.2024.3384389 · Cited by 14
“In addition, the rapid growth of the Internet of Things (IoT) services has triggered an explosive growth of data traffic at the edge, leading to the “sinking” of mobile Internet to the network edge [7].”
BackgroundGenerative Edge Intelligence for IoT-Assisted Vehicle Accident Detection: Challenges and Prospects
Liu, J., Liu, Y., Gao, K., et al. (2024) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2300282 · Cited by 14
“…massive data processing and analysis for edge devices has been introduced, which delivers intelligent services at the edge of IoT networks, specifically near data sources like mobile devices and process raw data, subsequently transmitting Jiahui Liu refined data to the centralized server [3].”
BackgroundNative Support of AI Applications in 6G Mobile Networks Via an Intelligent User Plane
Schwarzmann, S., Civelek, T. E., Iera, A., Corujo, D., Karetsos, G. T., Guerzoni, R., Abboud, O., Valenzuela, A. M., Trivisonno, R., Spina, M. G., et al. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570691 · Cited by 14
“Splitting NN models [14], [15] may offer significant advantages such as: (i) network latency and overhead reduction; (ii) overcoming computational limitation at the device or edge nodes [16], and (iii) protecting user privacy by transmitting partially processed rather than raw data [17].”
BackgroundSecure architecture for Industrial Edge of Things(IEoT): A hierarchical perspective
Li, P., Xia, J., Wang, Q., Zhang, Y., & Wu, M. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110641 · Cited by 16
Bring Intelligence among Edges: A Blockchain-Assisted Edge Intelligence Approach
Qiu, C., Wang, X., Yao, H., et al. (2020) · Globecom · doi:10.1109/globecom42002.2020.9348271 · Cited by 14
“Recently, the cooperation between edge computing and AI is encouraged, referred to as edge intelligence [1].”
BackgroundReconfigurable Intelligent Surface Assisted Edge Machine Learning
Huang, S., Wang, S., Wang, R., et al. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500445 · Cited by 14
“Mobile edge computing (MEC) is naturally well-suited for the AIoriented networks, and the marriage of mobile edge computing (MEC) and AI has given rise to a new research area, called “edge intelligence (EI)” or “edge AI” [1]–[4].”
BackgroundDynamic DNN Decomposition for Lossless Synergistic Inference
Zhang, B., Tian, X., Zhang, H., Li, T., Zhu, S., & Gu, J. (2021) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw53096.2021.00010 · Cited by 18
“Besides, prior works require re-partitioning the whole DNN to accommodate dynamics of computation resources and network bandwidth [12].”
MethodTowards a new paradigm for managing computing continuum applications
Pujol, V. C., Raith, P., & Dustdar, S. (2021) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi52975.2021.00032 · Cited by 17
“On-device computation can be used to train models in a Federated Learning setup on highly sensitive data without exposing sensitive data [24].”
MethodDPS: Dynamic Pricing and Scheduling for Distributed Machine Learning Jobs in Edge-Cloud Networks
Zhou, R., Wang, N., Huang, Y., Pang, J., & Chen, H. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3195765 · Cited by 16
“05] time slots, g p j is in [10, 100] milliseconds, mj is in [30, 575] MB, and D " js is in [1, 4] time slots ([10, 15] time slots for cloud).”
BackgroundThree-Dimensional Working Pose Estimation in Industrial Scenarios With Monocular Camera
Yu, Y., Li, H., Cao, J., & Luo, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3014930 · Cited by 14
EdgeNAS: Discovering Efficient Neural Architectures for Edge Systems
Luo, X., Liu, D., Kong, H., & Liu, W. (2020) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd50377.2020.00056 · Cited by 16
“The emerging edge intelligent systems are deemed to pave the way for future artificial intelligence (AI) [29].”
BackgroundMobile sink assisted data gathering for URLLC in IoT using a fuzzy logic system
Sefati, S. S., & Halunga, S. V. (2022) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom54372.2022.9858268 · Cited by 14
“The second method is to develop options in existing systems or even dedicated networks for allowing the essential applications and, then, extensions for non-essential ones [4].”
MethodEvent-Driven Deep Learning for Edge Intelligence (EDL-EI)
Shah, S. K., Tariq, Z., Lee, J., & Lee, Y. (2021) · Sensors · doi:10.3390/s21186023 · Cited by 16
“[4] presented an interesting co-inference architecture with device–edge interaction.”
BackgroundInfluentialAdaptive Random Forests for Energy-Efficient Inference on Microcontrollers
Daghero, F., Burrello, A., Xie, C., Benini, L., Calimera, A., Macii, E., Poncino, M., & Pagliari, D. J. (2021) · IFIP/IEEE International Conference on Very Large Scale Integration · doi:10.1109/vlsi-soc53125.2021.9606986 · Cited by 17
“unstable connectivity, and an enhanced data privacy, performing inference on end-nodes often results in a higher energy efficiency too, by avoiding the transmissions of large amounts of raw data through a power-hungry wireless link [2].”
BackgroundDing, Y., Fang, W. W., Liu, M., Wang, M., Cheng, Y., & Xiong, N. N. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.11.008 · Cited by 14
Load Balancing Mechanism for Edge-CloudBased Priorities Containers
Hadeed, W., & Abdullah, D. B. (2022) · International Journal of Wireless and Microwave Technologies · doi:10.5815/ijwmt.2022.05.01 · Cited by 13
Online Scheduling of CPU-NPU Co-inference for Edge AI Tasks
Lin, X., Liu, R., Xie, J., Wei, Q., Zhou, Z., Chen, X., Huang, Z., & Lu, G. (2023) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc55385.2023.10118755 · Cited by 14
“In response, edge intelligence [1] has emerged as a new paradigm to push AI tasks to the networks edges to process data in closer proximity to users and IoT devices.”
BackgroundSelf-citationAudio-based anomaly detection on edge devices via self-supervision and spectral analysis
Scudo, F. L., Ritacco, E., Caroprese, L., & Manco, G. (2023) · Journal of Intelligent Information Systems · doi:10.1007/s10844-023-00792-2 · Cited by 16
“Second, the collected data may contain sensitive and private information about workplaces or individuals; thus, the data transfer to external infrastructures could raise significant concerns for data safety and privacy leakages Sánchez et al. 2016; Caire et al. 2016; Zhou et al. 2019.”
Serving Graph Neural Networks With Distributed Fog Servers for Smart IoT Services
Zeng, L., Chen, X., Huang, P., Luo, K., Zhang, X., & Zhou, Z. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3293052 · Cited by 17
“To tame such intractability, a promising solution is to exploit available computing resources in proximity to data sources with the emerging fog computing 1 paradigm [22].”
MethodSelf-citationA Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks
Firdaus, M., Larasati, H. T., & Rhee, K.-H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.039487 · Cited by 16
Mughal, F. R., He, J., Zhu, N., Hussain, S., Zardari, Z. A., Mallah, G. A., Piran, M. J., & Dharejo, F. A. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.10.026 · Cited by 14
Enabling Smart Farming Through Edge Artificial Intelligence (AI)
Avanija, J., Rajyalakshmi, C., Madhavi, K. R., & Rao, B. N. K. (2024) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-2069-3.ch004 · Cited by 13
HyperSense: Hyperdimensional Intelligent Sensing for Energy‐Efficient Sparse Data Processing
Yun, S., Chen, H., Masukawa, R., Barkam, H. E., Ding, A., Huang, W., Rezvani, A., Angizi, S., & Imani, M. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202400228 · Cited by 15
“HDC, mimicking brain functionalities, offers advantages in efficiency and noise-tolerant computation [15], [38].”
BackgroundDong, L., Zhou, Y., Liu, L., Qi, Y., & Zhang, Y. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3450549 · Cited by 14
A framework for capability provisioning in B5G
Räisänen, V. (2020) · doi:10.1109/6gsummit49458.2020.9083831 · Cited by 13
“The combination of AI and edge computing has been called edge AI [9], and is supported by ML accelerators in edge computing platforms.”
MethodDong, Y., Zhao, P. F., Yu, H., Zhao, C., & Yang, S. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/467 · Cited by 17
“Recently, the emerging paradigm of collaborative Edge Intelligence (EI) rapidly draws significant interests from both academia [Zhou et al., 2019; Chen and Ran, 2019] and industry[Google, 2018; Microsoft, 2019], where the deployment of various Artificial Intelligence (AI) applications is pushed…”
InfluentialNoise Is Useful: Exploiting Data Diversity for Edge Intelligence
Zeng, Z., Liu, Y., Tang, W., et al. (2021) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2021.3051688 · Cited by 13
Towards Federated Learning in Edge Computing for Real-Time Traffic Estimation in Smart Cities
da Silva, M. V. S., Bittencourt, L. F., & Rivera, A. R. (2020) · Anais do Workshop de Computação Urbana (CoUrb) · doi:10.5753/courb.2020.12361 · Cited by 13
“ANNs consist of a series of layers that are capable of generating non-linear outputs based on the layer activations and input data [Zhou et al. 2019].”
MethodDistributed Slice Selection-Based Computation Offloading for Intelligent Vehicular Networks
Tang, J., Duan, Y., Zhou, Y., & Jin, J. (2021) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2021.3087355 · Cited by 13
Perspectives on Emerging Computation-in-Memory Paradigms
Rai, S., Liu, M., Gebregiorgis, A., Bhattacharjee, D., Chakrabarty, K., Hamdioui, S., Chattopadhyay, A., Trommer, J., & Kumar, A. S. (2021) · Design, Automation, and Test in Europe · doi:10.23919/date51398.2021.9473976 · Cited by 14
“In this regard, memristor-based computation-in-memory (CIM) has the potential to break the aforementioned challenge by replacing the traditional computing architecture by CIM architectures [14, 15] (as shown in Figure 1b).”
MethodTowards Efficient Inference: Adaptively Cooperate in Heterogeneous IoT Edge Cluster
Yang, X., Qi, Q., Wang, J., Guo, S., & Liao, J. (2021) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs51616.2021.00011 · Cited by 15
Efficient and Secure Deep Learning Inference in Trusted Processor Enabled Edge Clouds
Li, Y., Zeng, D., Gu, L., Chen, Q., Guo, S., Zomaya, A. Y., & Guo, M. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3187772 · Cited by 14
Security-Communication-Computation Tradeoff of Split Decisions for Edge Intelligence
Lyu, X., Liu, J., Ren, C., & Nan, G. (2023) · IEEE Wireless Communications · doi:10.1109/mwc.014.2200438 · Cited by 13
“Such computation overhead would be unaffordable for resource-constrained devices with stringent onboard energy consumption limitations, computation resources, and memory spaces [4].”
BackgroundEnergy and Latency Efficient Caching in Mobile Edge Networks: Survey, Solutions, and Challenges
Mohammed, L. B., Anpalagan, A., & Jaseemuddin, M. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10187-9 · Cited by 13
“The merge of MEN and AI is required such that MEN collaborate between edge devices and SBSs to serve users requests and AI simulate intelligent human behaviour in mobile devices by learning from previous data [118].”
BackgroundFedComm: Understanding Communication Protocols for Edge-based Federated Learning
Cleland, G., Wu, D., Ullah, R., & Varghese, B. (2022) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc56403.2022.00018 · Cited by 15
“com/qub-blesson/FedComm nodes generate large volume of data, which need to be analyzed to extract insight from them for automated decision making by machine learning techniques [11]; (ii) FL provides a distributed machine learning solution to extract knowledge from data in a privacy-preserving manner [12].”
BackgroundAddressing the Gap Between Training Data and Deployed Environment by On-Device Learning
Sunaga, K., Kondo, M., & Matsutani, H. (2023) · IEEE Micro · doi:10.1109/mm.2023.3314711 · Cited by 13
Learning-Based Online Transmission Path Selection for Secure Estimation in Edge Computing Systems
Wang, X., Chen, C., He, J., Zhu, S., & Guan, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3012090 · Cited by 14
“Recently, driving by the advancements of machine learning, learning-based methods are applied to the design of defensive strategies against attacks [19].”
BackgroundLearning Centric Power Allocation for Edge Intelligence
Wang, S., Wang, R., Hao, Q., Wu, Y., & Poor, H. V. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148872 · Cited by 12
“To address this challenge brought by MTC, a promising solution is the edge intelligence technique [3]–[7] that uses an intelligent edge to collect data generated from MTC devices and trains a machine learning model or fine-tunes a pre-trained model at the edge.”
BackgroundLow‐overhead inline deduplication for persistent memory
Chen, W., Chen, Z., Li, D., Liu, H., & Tang, Y. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4079 · Cited by 13
Edge Intelligence Empowered Cross-Modal Streaming Transmission
Gao, Y., Wei, X., Kang, B., & Chen, J. (2020) · IEEE Network · doi:10.1109/mnet.011.2000474 · Cited by 13
“Obviously, the combination of edge computing and AI, namely edge intelligence [8], brings a new paradigm shift to the big data era, which makes full use networking resources from the intelligence perspective.”
BackgroundMethodInfluentialSimpson, S. V., & Nagarajan, G. (2021) · Wireless Networks · doi:10.1007/s11276-021-02667-2 · Cited by 13
RetractedComplementing IIoT Services Through AI: Feasibility and Suitability
Banaie, F., & Hashemzadeh, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-76613-9_2 · Cited by 14
Luo, S., Chen, X., Wu, Q., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11343 · Cited by 13
“AS MOBILE and internet of things (IoT) devices have emerged in large numbers and are generating a massive amount of data [1], Machine Learning (ML) has been witnessed to go through a high-speed development due to big”
BackgroundSelf-citation
Metadata
- Authors
- Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J.
- Affiliations
- Sun Yat-sen University (CN)
- Arizona State University (US)
- Venue
- Proceedings of the IEEE
- Published
- 2019-08
- Volume
- 107
- Issue
- 8
- Pages
- 1738-1762
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2950865323
- Semantic Scholar
- 928cd808aba140ec298508df87c5579811ff2f41
- Cited by
- 2,455
- References
- 168
- Citations by year
- 2026: 455 · 2025: 352 · 2024: 332 · 2023: 363 · 2022: 370 · 2021: 341 · 2020: 215 · 2019: 26
- FWCI
- 130.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the IEEE (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Basic Research Program of China (2017YFB1001703)
- National Natural Science Foundation of China (U1711265, 61802449)
- Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07X355)
- Guangdong Natural Science Funds (2018A030313032)
- Fundamental Research Funds for the Central Universities (17lgjc40)
- Army Research Office (W911NF-16-1-0448)
- Defense Threat Reduction Agency (HDTRA1-13-1-0029)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- edge AI, edge computing, edge computing architecture, deep learning training, deep learning inference, Internet of Things, mobile computing
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