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.”
Flagged first, then by citations
28 of the 168 references couldn’t be matched to a known paper.
Research works printed but not linked: 11
- 0, 3 AI Trends for Enterprise Computing
- 0, Microsoft Interactive Cloud Gaming
- 0, Democratizing AI
- hsieh, 2017, Gaia: Geo-distributed machine learning approaching LAN speeds, Proc NSDI, 629
- 2018, 5 Trends Emerge in the Gartner Hype Cycle for Emerging Technologies
- 0, Cisco global cloud index Forecast and methodology 2016–2021
- 0, Accelerating AI on the Intelligent Edge
- 2017, IEC White Paper Edge Intelligence
- 0, Edge Intelligence for Industrial Internet of Things
- han, 2015, Learning both weights and connections for efficient neural network, Proc Adv Neural Inf Process Syst, 1135
- 2018, 5 Trends Emerge in the Gartner Hype Cycle for Emerging Technologies
Cited by
Sorted by citations
Items 401–500 of 2,413
Wireless for Machine Learning: A Survey
Hellström, H., da Silva, J. M. B., Amiri, M. M., Chen, M., Fodor, V., Poor, H. V., & Fischione, C. (2022) · doi:10.1561/9781638280071 · Cited by 55
Collective reinforcement learning based resource allocation for digital twin service in 6G networks
Huang, Z., Li, D., Cai, J., & Lu, H. C. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103697 · Cited by 36
A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors
Luo, B., Kou, Z., Han, C., Wu, J., & Liu, S. (2023) · Sensors · doi:10.3390/s23146276 · Cited by 37
“Data Preparation Data, computing power, and algorithms are the three fundamental elements of artificial intelligence [21,22].”
BackgroundToward Evolutionary Multitask Convolutional Neural Architecture Search
Zhou, X., Wang, Z., Feng, L., Liu, S., Wong, K., & Tan, K. C. (2023) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2023.3348475 · Cited by 36
“One notable example is the proliferation of billions of mobile and IoT devices, wherein it is projected that by 2022, 80% of IoT projects will incorporate deep learning models to develop edge intelligence applications [15].”
BackgroundMachine learning methods for service placement: a systematic review
Haddadha, P. K., Rezvani, M. H., Mollamotalebi, M., & Shankar, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10684-0 · Cited by 36
Mulo, J., Liang, H., Qian, M., Biswas, M., Rawal, B. S., Guo, Y., & Yu, W. (2025) · Future Internet · doi:10.3390/fi17030107 · Cited by 46
“Running computationally intensive DL models on these devices can rapidly drain their limited battery life, restricting their ability to provide continuous monitoring and insights [273,274].”
Chatterjee, B., Seo, D., Chakraborty, S., et al. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3036087 · Cited by 35
“This has given rise to the paradigm of “edge intelligence” (EI) [14], [15], which incorporates processing and/or machine learning capabilities at the sensor node.”
An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction
Yu, S., Chen, X., Wang, S., Pu, L., & Wu, D. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3007654 · Cited by 35
“To this end, multi-access edge computing (MEC) [8], [9], [10], has been proposed by the European Telecommunications Standards Institute (ETSI), which is viewed as an ideal solution to address the above challenges.”
Self-citationE-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI
Yang, L., Lu, Y., Cao, J., Huang, J., & Zhang, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3052195 · Cited by 36
Low-Power Memristor-Based Computing for Edge-AI Applications
Singh, A., Diware, S., Gebregiorgis, A., Bishnoi, R., Catthoor, F., Joshi, R., & Hamdioui, S. (2021) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas51556.2021.9401226 · Cited by 38
Serving at the Edge: An Edge Computing Service Architecture Based on ICN
Fan, Z., Yang, W., Wu, F., Cao, J. X., & Shi, W. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3464428 · Cited by 35
“With the adventure of edge computing, to improve the real-time responsiveness of AI services, machine learning models are usually deployed to the edge of the network to implement edge intelligence [8, 46].”
BackgroundModuloNET: Neural Networks Meet Modular Arithmetic for Efficient Hardware Masking
Dubey, A., Ahmad, A., Pasha, M. A., Cammarota, R., & Aysu, A. (2021) · IACR Transactions on Cryptographic Hardware and Embedded Systems · doi:10.46586/tches.v2022.i1.506-556 · Cited by 38
Borsci, S., Lehtola, V., Nex, F., Yang, M. Y., Augustijn, E.-W., Bagheriye, L., Brüne, C., Kounadi, O., Li, J. J., Moreira, J., et al. (2022) · AI & Society · doi:10.1007/s00146-021-01383-x · Cited by 44
“The growing need for retrieving heterogeneous information and operating upon it to ensure AI multi-modal learning inspires researchers toward the idea that to progress AI capabilities computing should be on the edge, intended as distributed computing in which data are captured near the source (Chen et al. 2019; Zhou e…”
BackgroundTortorella, Y., Bertaccini, L., Luca Benini, C., Rossi, D., & Conti, F. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.07.002 · Cited by 38
Dynamic AI-IoT: Enabling Updatable AI Models in Ultralow-Power 5G IoT Devices
Alselek, M., Calero, J. M. A., & Wang, Q. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3340858 · Cited by 34
“This extension empowers real-time decision-making while simultaneously reducing latency [16].”
BackgroundEnergy-Efficient Distributed Spiking Neural Network for Wireless Edge Intelligence
Liu, Y., Qin, Z., & Li, G. Y. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3374549 · Cited by 43
“T HE convergence of artificial intelligence (AI) and edge computing, referred to as edge intelligence, has gained considerable research attention [1].”
BackgroundEdge Intelligence for Industrial IoT: Opportunities and Limitations
Savaglio, C., Mazzei, P., & Fortino, G. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.01.039 · Cited by 34
Shen, S., Cai, C., Shen, Y., Wu, X., Ke, W., & Yu, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3387090 · Cited by 34
Lamaakal, I., Ouahbi, I., El Makkaoui, K., Maleh, Y., Pławiak, P., & Alblehai, F. (2024) · IEEE Access · doi:10.1109/access.2024.3406631 · Cited by 34
“The emergence of TinyML [1], [2], [3], [4], [55], [56], [58], [60] represents a significant breakthrough in technology, blending the ever-growing needs of data-driven applications with the constraints of low-power, embedded devices.”
BackgroundDeep reinforcement learning-based dynamical task offloading for mobile edge computing
Xie, B., & Cui, H. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06603-x · Cited by 34
Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare
Zhang, F., Zhai, D., Bai, G., Jiang, J., Ye, Q., Ji, X., & Liu, X. (2025) · Nature Communications · doi:10.1038/s41467-025-58055-3 · Cited by 43
“From a technical perspective, the resource heterogeneity across different participating clients involves variations in processing power, memory and communication capabilities 21 .”
Inam, S. A. (2025) · Discover Public Health · doi:10.1186/s12982-025-01167-4 · Cited by 43
“The dependence on large data sets, however, raises concerns around privacy and ownership of data, especially in economically disadvantaged areas that may contain sensitive medical data [148].”
Zhang, K., Huang, W. K., Hou, X., Xu, J., Su, R., & Xu, H. (2021) · Applied Sciences · doi:10.3390/app11031251 · Cited by 35
“This paper mainly explores the intelligent fault diagnosis method and high-speed train applications based on intelligent edge computing [8].”
A journey towards fully autonomous driving - fueled by a smart communication system
Khan, M. A., Sayed, H. E., Malik, S., Zia, M. T., Alkaabi, N., & Khan, J. (2022) · Vehicular Communications · doi:10.1016/j.vehcom.2022.100476 · Cited by 37
Amplify-and-Forward Relaying for Hierarchical Over-the-Air Computation
Wang, F., Xu, J., Lau, V. K. N., & Cui, S. R. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3185074 · Cited by 34
“In distributed edge learning (particularly the federated edge learning) [9], [10], the edge server coordinates multiple WDs to train shared machine learning models iteratively, and in each iteration, the edge server needs to update the global machine learning model parameters by computing the (weighted) mean values of…”
MethodVirtualizing AI at the Distributed Edge towards Intelligent IoT Applications
Campolo, C., Genovese, G., Iera, A., & Molinaro, A. (2021) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan10010013 · Cited by 33
“Recently, the availability of edge computing solutions, coupled with the need to build inference and make intelligent decisions as fast as possible, contributed to the marriage of edge computing and AI, leading to the so-called edge AI paradigm [2,4].”
BackgroundMethodEdge-Based Collaborative Training System for Artificial Intelligence-of-Things
Jin, Y., Huang, B., Yan, Y., Huan, Y., Xu, J., Li, S., Gope, P., Xu, L. D., Zou, Z., & Zheng, L. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3147831 · Cited by 35
Review on Offloading of Vehicle Edge Computing
Wang, M., Yi, H., Jiang, F., Lin, L., & Gao, M. (2022) · Journal of Artificial Intelligence and Technology · doi:10.37965/jait.2022.0120 · Cited by 33
“proposed an efficient asynchronous federated learning mechanism for edge network computing, which reduced data transmission delay [60].”
Backgrounddevi, O. R., Webber, J. L., Mehbodniya, A., Chaitanya, M., Jawarkar, P. S., Soni, M., & Miah, S. (2022) · Scientific Programming · doi:10.1155/2022/1473901 · Cited by 30
“Specialists should painstakingly concentrate on step-by-step instructions to understand the various leveled edge cloud coordinated effort prototype along with a disseminated edge-cooperation prototype, as it is consisting of its blend as well as shift component underneath the brought together engineering, also as this…”
BackgroundIntegration of SCADA and Industrial IoT: Opportunities and Challenges
Nechibvute, A., & Mafukidze, H. D. (2023) · IETE Technical Review · doi:10.1080/02564602.2023.2246426 · Cited by 37
Deep random forest with ferroelectric analog content addressable memory
Yin, X., Müller, F., Laguna, A. F., Li, C., Huang, Q., Shi, Z., Lederer, M., Laleni, N., Deng, S., Zhao, Z., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk8471 · Cited by 47
The Internet of Things in the Era of Generative AI: Vision and Challenges
Wang, X., Wan, Z., Hekmati, A., Zong, M., Alam, S., Zhang, M., & Krishnamachari, B. (2024) · IEEE Internet Computing · doi:10.1109/mic.2024.3443169 · Cited by 62
Boosting Edge Intelligence With Collaborative Cross-Edge Analytics
Jin, H., Jia, L., & Zhou, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3034891 · Cited by 32
“To address this issue, which is known as the “last mile of AI,” edge intelligence [4]–[6] is emerging as a promising solution to materialize the vision of AIoT.”
BackgroundMethodSelf-citationStable and compact design of Memristive GoogLeNet Neural Network
Ran, H., Wen, S., Shi, K., & Huang, T. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.122 · Cited by 33
EICache: A learning-based intelligent caching strategy in mobile edge computing
Tang, B., & Kang, L. (2021) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01266-4 · Cited by 33
“Machine learning is the study of computer algorithms that can be improved automatically through experience, and it is an effective method to achieve prediction or classification goals [25].”
MethodWhen machine learning meets Network Management and Orchestration in Edge-based networking paradigms
Shahraki, A., Ohlenforst, T., & Kreyß, F. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103558 · Cited by 35
An efficient DNN splitting scheme for edge-AI enabled smart manufacturing
Gauttam, H., Pattanaik, K. K., Bhadauria, S., Nain, G., & Prakash, P. B. (2023) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100481 · Cited by 31
HSFL: Efficient and Privacy-Preserving Offloading for Split and Federated Learning in IoT Services
Deng, R., Du, X., Lu, Z., Duan, Q., Huang, S.-C., & Wu, J. (2023) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws60048.2023.00084 · Cited by 32
Learning-Based Edge-Device Collaborative DNN Inference in IoVT Networks
Xu, X., Yan, K., Han, S., Wang, B., Tao, X., & Zhang, P. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3317785 · Cited by 32
“Furthermore, to empower massive mobile and IoT devices with AI capability close to the end devices, mobile edge AI or edge intelligence attracts lots of research attentions [12], [13], [14].”
BackgroundAdaptive Device-Edge Collaboration on DNN Inference in AIoT: A Digital-Twin-Assisted Approach
Hu, S., Li, M., Gao, J., Zhou, C., & Shen, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3336600 · Cited by 34
“Instead of determining the collaboration for each DNN inference task only upon its generation, multi-step decision making is performed during the on-device inference to adapt to the dynamic computing workload status at the device and the edge server.”
MethodSustainable Mobility in B5G/6G: V2X Technology Trends and Use Cases
Roger, S., Botella, C., Martín‐Sacristán, D., et al. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3375451 · Cited by 32
“In addition, MEC can be exploited to implement AI-based applications at distributed edge devices, pushing the network intelligence at the edge through the concept of edge-AI [18], [32], [33].”
BackgroundEdge Intelligence for Mission Cognitive Wireless Emergency Networks
Wang, L., Zhang, J., Chuan, J., Ma, R., & Fei, A. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900418 · Cited by 32
“By leveraging recent developments in edge computing and edge learning [6], edge servers will play pivotal roles in this architecture.”
BackgroundSelf-citationLiu, S., Du, J., Nan, K., Zhou, Z., Liu, H., Wang, Z., & Lin, Y. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2999956 · Cited by 37
“And consequently, they shrink the computation, storage, latency, and energy overhead on a target platform [6], [7].”
BackgroundSelf-citationQoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations
Hudson, N., Khamfroush, H., & Lucani, D. E. (2021) · Proceedings/Proceedings - International Conference on Computer Communications and Networks · doi:10.1109/icccn52240.2021.9522156 · Cited by 32
“Another proposed idea is to consider a EI model’s architecture being split across different tiers of the MEC architecture (e.g., one half is run on the edge and the other on the central cloud) [24].”
Nguyen, T. V., An, J., & Min, K. (2021) · Micromachines · doi:10.3390/mi12070791 · Cited by 31
Automatic recognition method of cow ruminating behaviour based on edge computing
Shen, W., Sun, Y., Zhang, Y., et al. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106495 · Cited by 32
Smart Zero-Touch Management of UAV-Based Edge Network
Grasso, C., Raftopoulos, R., & Schembra, G. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3160858 · Cited by 32
“This way, while 5G represents the evolution from the IoT paradigm to the new paradigm of Internet of Intelligent Things (IoIT) [6], enabled by cloud AI, 6G will represent the ultimate shift towards the revolutionary paradigm of Intelligent Internet of Intelligent Things (IIoIT), enabled by Edge AI [8]–[9].”
BackgroundMobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)
Qayyum, T., Trabelsi, Z., Malik, A. W., et al. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00345-y · Cited by 31
“A massive amount of data can be transferred to edge servers to train machine learning models, perform complex computations, and temporarily cache the data [5–8].”
BackgroundStochastic Cumulative DNN Inference With RL-Aided Adaptive IoT Device-Edge Collaboration
Qu, K., Zhuang, W., Wu, W. R., Li, M., Shen, X., Li, X., & Shi, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3280746 · Cited by 32
Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes
Daghero, F., Burrello, A., Macii, E., Montuschi, P., Poncino, M., & Pagliari, D. J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3286276 · Cited by 40
“Moreover, transmitting a constant stream of data to the cloud is an energy-hungry operation, which can severely affect the battery life of the device [6].”
BackgroundFedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge
Luopan, Y., Han, R., Zhang, Q., Liu, C. H., Wang, G., & Chen, L. Y. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00033 · Cited by 37
Optimized CNN Architectures Benchmarking in Hardware-Constrained Edge Devices in IoT Environments
Rosero-Montalvo, P. D., Tözün, P., & Hernández, W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3369607 · Cited by 31
Benchmarking Edge AI Platforms for High-Performance ML Inference
Jayanth, R., Gupta, N., & Prasanna, V. K. (2024) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec62836.2024.10938499 · Cited by 42
Edge-Based V2X Communications With Big Data Intelligence
Guleng, S., Wu, C., Liu, Z., & Chen, X. (2020) · IEEE Access · doi:10.1109/access.2020.2964707 · Cited by 29
“Recently, mobile edge computing (MEC) based approaches attract great interest in solving the performance problem in dynamic vehicular networks by caching the content near the vehicles or conducting some processing in vicinity [25]–[31].”
BackgroundSelf-citationScission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks
Lockhart, L., Harvey, P., Imai, P., Willis, P., & Varghese, B. (2020) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc48980.2020.00044 · Cited by 35
“Recently, distributed execution of the DNN across the cloud and resources at the edge of the network, within the edge computing paradigm [3], [4], [5], has been found to be beneficial [6], [7].”
BackgroundMethodInfluentialBiQGEMM: Matrix Multiplication with Lookup Table for Binary-Coding-Based Quantized DNNs
Jeon, Y., Park, B., Kwon, S. J., Kim, B., Yun, J., & Lee, D. (2020) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41405.2020.00099 · Cited by 35
A deep and systematic review of the intrusion detection systems in the fog environment
Lizhi, Y., Yin, M., & Darbandi, M. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4632 · Cited by 35
“Authors Accuracy & IDS precision Detection delay Detection rates Energy consumption Power consumption CPU time CPU usage Overhead Reaction time Recall rate Time complexity [67] * * * * * [68] * * [69] * * * * [76] * [70] * [49] * * * [77] * * * * [75] * [26] [70] * * * * * * [66] * * * * [76] * * [54] * [26] * [53] *…”
BackgroundMethodA Random Forest Approach to Body Motion Detection: Multisensory Fusion and Edge Processing
Kianoush, S., Savazzi, S., Rampa, V., da Costa, L. F., & Tolochenko, D. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3232085 · Cited by 30
“To address these issues, prior works about ML on edge devices focused on deep learning (DL) methods [16], [17], [18].”
BackgroundMethodMimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Sun, Y., Mao, Y., & Zhang, J. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3338021 · Cited by 33
Self-citationContainerization in Edge Intelligence: A Review
Urblík, Ľ., Kajáti, E., Papcun, P., & Zolotová, I. (2024) · Electronics · doi:10.3390/electronics13071335 · Cited by 36
Integrating IoT and 6G: Applications of Edge Intelligence, Challenges, and Future Directions
He, Q., Lin, J., Fang, H., Wang, X., Huang, M., Yi, X., & Yu, K. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3586152 · Cited by 39
Xiao, K., Shi, W., Gao, Z., Yao, C., & Qiu, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2984553 · Cited by 30
“EC uses an open platform that integrates network, computing, and storage to provide services [4].”
BackgroundRecent Progress on Memristive Convolutional Neural Networks for Edge Intelligence
Qin, Y., Bao, H., Wang, F., Chen, J., Li, Y., & Miao, X. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000114 · Cited by 37
Enabling Low Latency Edge Intelligence based on Multi-exit DNNs in the Wild
Huang, Z., Dong, F., Shen, D., et al. (2021) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs51616.2021.00075 · Cited by 30
“Currently, a widely adopted solution is to process such computation-intensive DNNs inference tasks with edge computing by providing a variety of resources to end devices in close proximity [3–7].”
Self-citationChen, S., Wang, X., Zhou, P., Wu, W., Lin, W., & Wang, Z. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3146871 · Cited by 31
Kansal, P., Kumar, M., & Verma, O. P. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04338-1 · Cited by 31
MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks
Sun, Y., Xie, B., Zhou, S., & Niu, Z. (2022) · IEEE Communications Magazine · doi:10.1109/mcom.001.2200252 · Cited by 32
“Integrating AI and edge computing technologies, edge intelligence is considered as an emerging paradigm to drive these applications [1].”
InfluentialCollaborative Edge Intelligence Service Provision in Blockchain Empowered Urban Rail Transit Systems
Liang, H., Zhu, L., & Yu, F. R. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3294400 · Cited by 34
Xu, X., Xu, B., Han, S., Dong, C., Xiong, H., Meng, R., & Zhang, P. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3305011 · Cited by 32
“By marrying edge computing to AI, edge intelligence (EI) utilizes most resources to enable edges to execute AI algorithms and gain AI insights [27].”
Task-Oriented 6G Native-AI Network Architecture
Yang, Y., Wu, J., Chen, T., Peng, C., Wang, J., Deng, J., Tao, X., Liu, G., Li, W., Li, Y., et al. (2023) · IEEE Network · doi:10.1109/mnet.2023.3321464 · Cited by 29
From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
Hlophe, M. C., & Maharaj, B. T. (2023) · Applied Sciences · doi:10.3390/app132413262 · Cited by 29
Ye, S., Zeng, L., Chu, X., Xing, G., & Chen, X. (2024) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3636534.3649363 · Cited by 36
“Edge environments are typically heterogeneous [66], with facilities ranging from small devices and gateways [10] to much powerful cloudlets [46].”
BackgroundSelf-citationWhen the Edge Meets Transformers: Distributed Inference with Transformer Models
Hu, C., & Li, B. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00017 · Cited by 34
“To keep the models intact and guarantee the quality of inference, the new paradigm of distributed inference has been proposed, which distributes the workload across multiple edge devices to accelerate the inference process [9].”
MethodIntelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications
Cajas, S. A., Samanta, J., Suárez‐Cetrulo, A. L., & Carbajo, R. S. (2025) · Future Internet · doi:10.3390/fi17090417 · Cited by 47
“In parallel, federated learning has emerged as a promising paradigm for distributing intelligent model training while preserving data locality in network environments [17,18], though challenges persist in managing energy, model updates, and system robustness across network topologies [1,3,5,19,20].”
InfluentialChen, X., Yu, L., Wang, T., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2984329 · Cited by 29
“Most protocols focus on energy efficiency, which is extremely important in WSNs, and even in other fields, such as RFID [79]–[82], mobile edge computing systems [83], [84], device-to-device communications [85], [86],”
BackgroundSelf-citationInternet of things‐fog computing‐based framework for smart disaster management
Aljumah, A. A., Kaur, A., Bhatia, M., & Ahanger, T. A. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4078 · Cited by 29
Impact of Asymmetric Weight Update on Neural Network Training With Tiki-Taka Algorithm
Lee, C., Noh, K., Ji, W., Gokmen, T., & Kim, S. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.767953 · Cited by 29
Hao, J., Subedi, P., Ramaswamy, L. M., & Kim, I. K. (2022) · ACM Transactions on Internet Technology · doi:10.1145/3546192 · Cited by 29
Energy Efficient Task Caching and Offloading in UAV-Enabled Crowd Management
Wu, G., Liu, Q., Xu, J., Miao, Y., & Pustišek, M. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3182779 · Cited by 29
“Edge intelligence as a key technology of B5G/6G networks is expected to deploy AI capability on edge computing [8]–[11].”
BackgroundDistributed artificial intelligence: Taxonomy, review, framework, and reference architecture
Janbi, N. F., Katib, I. A., & Mehmood, R. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200231 · Cited by 30
A Survey on Hardware Accelerator Design of Deep Learning for Edge Devices
Samanta, A., Hatai, I., & Mal, A. K. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-11443-2 · Cited by 28
“Zhou et al. [68] provides a comprehensive overview of edge AI, including distributed DNN architectures, enabling and emerging techniques/technologies (e.g., federated learning, model compression, and partition) for edge intelligence training and inference models, systems, and frameworks at the…”
Cost-Effective Edge Data Caching With Failure Tolerance and Popularity Awareness
Luo, R., Zhang, Z., He, Q., Xu, M., Chen, F., Dai, X., Wu, S., & Jin, H. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3531967 · Cited by 28
Energy efficient task scheduling for heterogeneous multicore processors in edge computing
Liu, Y., Qu, H., Chen, S., & Feng, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-92604-6 · Cited by 38
Model-size reduction for reservoir computing by concatenating internal states through time
Sakemi, Y., Morino, K., Leleu, T., & Aihara, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-78725-0 · Cited by 30
CRIME: Input-Dependent Collaborative Inference for Recurrent Neural Networks
Pagliari, D. J., Chiaro, R., Macii, E., & Poncino, M. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.3021199 · Cited by 30
“The spread of DL has been partially fueled by the proliferation of IoT sensors and smart devices generating massive amounts of data [2], [3].”
BackgroundFast Implementation of 4-bit Convolutional Neural Networks for Mobile Devices
Trusov, A. V., Limonova, E. E., Slugin, D., et al. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412841 · Cited by 28
“New paradigms tend to push computing tasks and services from the network core to the network edge (edge computing and edge intelligence) [5] and even to end devices.”
BackgroundYang, B., Wu, D., Wang, H., Gao, Y., & Wang, R. (2020) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2020.3044566 · Cited by 28
“In contrast to artificial intelligence based on supercomputing (e.g., AlphaStar), the edge intelligence is gaining research attention since IIoT and tactile Internet communities are seeking ultra-reliable low-latency but computationally powerful solutions [40].”
BackgroundSeo, Y.-J., Lee, J., Hwang, J., et al. (2021) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2021.3057114 · Cited by 28
“Various forms of deep learning-based object detection algorithms [2] have been designed, and have boosted MAR services.”
BackgroundDeep-Reinforcement-Learning-Based Latency Minimization in Edge Intelligence Over Vehicular Networks
Zhao, N., Wu, H., Yu, F. R., Wang, L., Zhang, W., & Leung, V. C. M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3078480 · Cited by 27
Automated Ensemble for Deep Learning Inference on Edge Computing Platforms
Bai, Y., Chen, L., Abdel-Mottaleb, M. S., & Xu, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3102945 · Cited by 27
Lin, H., Wu, Z., Liu, L., Wang, D., Zhao, X., Cheng, L., Lin, Y., Wang, Z., Xu, X., Xu, H., et al. (2021) · Advanced Materials Technologies · doi:10.1002/admt.202100745 · Cited by 28
Zhang, J., Wang, P., & Zhang, N. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3109006 · Cited by 28
Self-citationA secure and flexible edge computing scheme for AI-driven industrial IoT
Zhao, Y. L., Hu, N., Zhao, Y., & Zhu, Z. (2021) · Cluster Computing · doi:10.1007/s10586-021-03400-6 · Cited by 28
“enabling AI functions to be performed near the data source, thereby providing flexible, economical, and higher-performance services [6].”
BackgroundDistributed Fog Computing and Federated-Learning-Enabled Secure Aggregation for IoT Devices
Liu, Y., Dong, Y., Wang, H., Jiang, H., & Xu, Q. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176305 · Cited by 30
“The survey by Zhou et al. [52] summarized recent studies on edge intelligence and listed FL as one of the most uprising technologies for distributed deep neural network (DNN) training at the edge.”
BackgroundEfficient Personalized Speech Enhancement Through Self-Supervised Learning
Sivaraman, A., & Kim, M. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3181782 · Cited by 28
“And although modern GPU technology enables training neural networks with millions of learnable parameters [13], bigger models are infeasible to deploy on low-resource devices [14].”
BackgroundNetwork for Distributed Intelligence: a Survey and Future Perspectives
Campolo, C., Iera, A., & Molinaro, A. (2023) · IEEE Access · doi:10.1109/access.2023.3280411 · Cited by 33
“In addition, model splitting protects user privacy by transmitting partially processed data rather than transmitting raw data [35].”
BackgroundAI on the Edge: Architectural Alternatives
John, M. M., Olsson, H. H., & Bosch, J. (2020) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa51224.2020.00015 · Cited by 34
“Based on the amount and path length of data offloading in [12], edge intelligence rates at six levels where training and inference is entirely cloud-based and gradually focuses on edge going forward.”
BackgroundLearning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment
Zhou, L., Hong, Y., Wang, S., Han, R., Li, D., Wang, R., & Hao, Q. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3047149 · Cited by 25
“With the edge computing platform, various machine learning applications can reside at the edge, giving rise to an emerging paradigm termed edge intelligence [3].”
BackgroundHohman, F., Kery, M. B., Ren, D., & Moritz, D. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642109 · Cited by 33
“2.3.2 On-device Large Language Models and Foundation Models.”
BackgroundMethodInfluentialRahmati, I., Shah‐Mansouri, H., & Movaghar, A. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3556809 · Cited by 33
“M OBILE edge computing (MEC) [1] has emerged as a promising technological solution to overcome the challenges faced by mobile devices (MDs) when performing high computational tasks, such as real-time data processing and artificial intelligence applications [2] [3].”
Background
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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