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Cited by 171Advanced Neural Network ApplicationsIoT and Edge/Fog ComputingDomain Adaptation and Few-Shot Learning

The DOI matches its Crossref record; 213 of the 278 references it cites were found in a registry or an index; none of the 100 sources its claims cite is checked for support yet.

Bringing AI to edge: From deep learning’s perspective

Open access
Liu, Di, Kong, Hao, Luo, Xiangzhong, Liu, Weichen, Subramaniam, Ravi·2022·Neurocomputing
DOI 10.1016/j.neucom.2021.04.141
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Crossref · 10.1016/j.neucom.2021.04.141

Cites

213 of 213 references verified0 citation errors · 0 not checkable

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Show all 80 claims

  • It may state two separate claims as one. 38
  • Probably not a substantial claim. 22
52 of 52

  • The paper relies on this without support.

    At present, edge intelligence systems are used mainly for computer vision tasks.

    “currently computer vision models are the major application for edge intelligence systems”

  • The paper relies on this without support.

    Real-world edge deployments face rapidly changing conditions.

    “Edge systems installed in real world operate in highly dynamic environment”

  • The paper relies on this without support.

    Manual design of lightweight DNN models depends heavily on expert knowledge and requires lengthy hyperparameter exploration.

    “The manual design of lightweight DNN models is considerably dependent on experts' knowledge and also needs a time-consuming hyperparameter exploration.”

  • The paper relies on this without support.

    Different layers have different functions.

    “different layers have different functions”

  • The paper relies on this without support.

    Reductions in FLOPs and compression ratio do not directly translate into performance improvement.

    “the reduced FLOPs and compression ratio cannot directly translate to the performance improvement.”

  • The paper relies on this without support.

    Different hardware architectures demonstrate different degrees of parallelism.

    “different hardware architectures demonstrate different degrees of parallelism”

  • The paper relies on this without support.

    The design space for DNN is too large for exhaustive search.

    “In addition, design space for DNN is too large to have an exhaustive search.”

  • The paper relies on this without support.

    There is a lack of measurement of trade-offs between metrics, leading to high search cost or sub-optimal design.

    “However, we still lack the measurement of the trade-off between each metric and this will lead to either high searching cost or sub-optimal design result.”

  • The paper relies on this without support.

    Different hardware accelerators have diverse underlying characteristics, and most DNNs were designed without considering underlying hardware features.

    “However, different hardware accelerators features diverse underlying characteristics, and the majority of DNNs were designed without consideration of underlying hardware features.”

  • The paper relies on this without support.

    The majority of works on deep learning for edge systems target computer vision tasks such as image classification and object detection.

    “Currently, the majority of works regarding deep learning on edge systems target computer vision tasks, i.e., image classification and object detection.”

  • The paper relies on this without support.

    On-device learning is challenging because training is more computation-intensive and memory-hungry than inference, while edge systems are resource-constrained.

    “Ondevice learning is a challenging task, because training is a more computation-intensive and memory-hungry procedure compared to inference, whereas edge systems are resourceconstrained in many settings.”

  • The paper relies on this without support.

    Limited energy supply of some edge systems makes on-device learning even more difficult.

    “Moreover, limited energy supply of some edge systems will make this issue even more difficult.”

  • The paper relies on this without support.

    The success of DNN models heavily relies on high-quality and large-scale datasets.

    “The success of DNN models heavily relies on high-quality and large-scale datasets, such as ImageNet”

  • The paper relies on this without support.

    For some edge applications, collecting and labeling massive data is difficult or expensive.

    “for some edge applications, e.g., edge surveillance systems in wild life, defect detection in manufacturing process, etc, it is difficult or expensive to collect massive amount of data and label them to train a good model.”

  • The paper relies on this without support.

    During long-term operation, edge systems are likely to collect data with different distributions or new classes, requiring model updates.

    “during the long-term operation, edge systems are likely to collect data with different distributions from the original training data or data pertaining to a new class which is not included in the original training data, we may need to update the model on edge systems”
15 of 15

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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.

  • NAS search-cost column conflicts with its stated unit

    Minor

    The table introduction specifies GPU hours, but the column heading labels the same costs as GPU days.

  • Figure 11 has two different architecture counts

    Minor

    The discussion says Figure 11 measures 100000 architectures, while its caption says 10000.

  • BinaryConnect year differs from its reference

    Minor

    Table V gives 2016 for BinaryConnect [111], while the corresponding reference gives 2015.

  • BranchyNet year differs from its reference

    Minor

    Table VIII dates BranchyNet to 2017, but the cited conference entry is dated 2016.

  • Li et al. pruning scope differs between table and prose

    Minor

    Table IV classifies Li et al. [87] as layer pruning, whereas the accompanying discussion calls it global pruning.

5 of 5

Citations

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65 of the 278 references couldn’t be matched to a known paper.

Research works printed but not linked: 37

  1. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: P.L. Bartlett, F.C.N. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, 2012, pp. 1106–1114. URL:https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
  2. Goodfellow, 2016, vol. 1
  3. Ieee computer society’s top 12 technology trends for 2020, URL: https://www.computer.org/press-room/2019-news/ieee-computer-societys-top-12-technology-trends-for-2020, access: 2020-5-14.
  4. Wei, 2021, Wireless edge caching based on content similarity in dynamic environments, Journal of Systems Architecture, 102000
  5. Choudhary, 2020, A comprehensive survey on model compression and acceleration, Artificial Intelligence Review, 1
  6. Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, M. Bennamoun, Deep learning for 3d point clouds: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence.
  7. M. Feurer, A. Klein, K. Eggensperger, J.T. Springenberg, M. Blum, F. Hutter, Efficient and robust automated machine learning, in: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7–12, 2015, Montreal, Quebec, Canada, 2015, pp. 2962–2970. URL:https://proceedings.neurips.cc/paper/2015/hash/11d0e6287202fced83f79975ec59a3a6-Abstract.html.
  8. J. Wang, T.A. Bohn, C.X. Ling, Pelee: A real-time object detection system on mobile devices, in: S. Bengio, H.M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3–8, 2018, Montréal, Canada, 2018, pp. 1967–1976. URL:https://proceedings.neurips.cc/paper/2018/hash/9908279ebbf1f9b250ba689db6a0222b-Abstract.html.
  9. S. Ren, K. He, R.B. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks, in: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7–12, 2015, Montreal, Quebec, Canada, 2015, pp. 91–99. URL:https://proceedings.neurips.cc/paper/2015/hash/14bfa6bb14875e45bba028a21ed38046-Abstract.html.
  10. M. Denil, B. Shakibi, L. Dinh, M. Ranzato, N. de Freitas, Predicting parameters in deep learning, in: C.J.C. Burges, L. Bottou, Z. Ghahramani, K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5–8, 2013, Lake Tahoe, Nevada, United States, 2013, pp. 2148–2156. URL: https://proceedings.neurips.cc/paper/2013/hash/7fec306d1e665bc9c748b5d2b99a6e97-Abstract.html.
  11. Y. LeCun, J.S. Denker, S.A. Solla, Optimal brain damage, in: Advances in neural information processing systems, 1990, pp. 598–605.
  12. S. Han, J. Pool, J. Tran, W.J. Dally, Learning both weights and connections for efficient neural network, in: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7–12, 2015, Montreal, Quebec, Canada, 2015, pp. 1135–1143. URL: https://proceedings.neurips.cc/paper/2015/hash/ae0eb3eed39d2bcef4622b2499a05fe6-Abstract.html.
  13. S. Han, H. Mao, W.J. Dally, Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, International Conference on Learning Representations (ICLR).
  14. D. Molchanov, A. Ashukha, D.P. Vetrov, Variational dropout sparsifies deep neural networks, in: D. Precup, Y.W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017, Vol. 70 of Proceedings of Machine Learning Research, PMLR, 2017, pp. 2498–2507. URL: http://proceedings.mlr.press/v70/molchanov17a.html.
  15. Deb, 2001, vol. 16
  16. H. Li, A. Kadav, I. Durdanovic, H. Samet, H.P. Graf, Pruning filters for efficient convnets, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24–26, 2017, Conference Track Proceedings, OpenReview.net, 2017. URL: https://openreview.net/forum?id=rJqFGTslg.
  17. W. Wen, C. Wu, Y. Wang, Y. Chen, H. Li, Learning structured sparsity in deep neural networks, in: D.D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, R. Garnett (Eds.), Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5–10, 2016, Barcelona, Spain, 2016, pp. 2074–2082. URL: https://proceedings.neurips.cc/paper/2016/hash/41bfd20a38bb1b0bec75acf0845530a7-Abstract.html.
  18. M. Courbariaux, Y. Bengio, J. David, Binaryconnect: Training deep neural networks with binary weights during propagations, in: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7–12, 2015, Montreal, Quebec, Canada, 2015, pp. 3123–3131. URL: https://proceedings.neurips.cc/paper/2015/hash/3e15cc11f979ed25912dff5b0669f2cd-Abstract.html.
  19. A. Tarvainen, H. Valpola, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, in: I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S.V.N. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, 2017, pp. 1195–1204. URL: https://proceedings.neurips.cc/paper/2017/hash/68053af2923e00204c3ca7c6a3150cf7-Abstract.html.
  20. H. Cai, C. Gan, T. Wang, Z. Zhang, S. Han, Once-for-all: Train one network and specialize it for efficient deployment, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26–30, 2020, OpenReview.net, 2020. URL: https://openreview.net/forum?id=HylxE1HKwS.
  21. C. Li, Z. Yu, Y. Fu, Y. Zhang, Y. Zhao, H. You, Q. Yu, Y. Wang, C. Hao, Y. Lin, HW-nas-bench: Hardware-aware neural architecture search benchmark, in: International Conference on Learning Representations, 2021. URL: https://openreview.net/forum?id=_0kaDkv3dVf.
  22. J. Lin, Y. Rao, J. Lu, J. Zhou, Runtime neural pruning, in: I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S.V.N. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, 2017, pp. 2181–2191. URL: https://proceedings.neurips.cc/paper/2017/hash/a51fb975227d6640e4fe47854476d133-Abstract.html.
  23. B.E. Bejnordi, T. Blankevoort, M. Welling, Batch-shaping for learning conditional channel gated networks, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26–30, 2020, OpenReview.net, 2020. URL: https://openreview.net/forum?id=Bke89JBtvB.
  24. T. Bolukbasi, J. Wang, O. Dekel, V. Saligrama, Adaptive neural networks for efficient inference, in: D. Precup, Y.W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017, Vol. 70 of Proceedings of Machine Learning Research, PMLR, 2017, pp. 527–536. URL: http://proceedings.mlr.press/v70/bolukbasi17a.html.
  25. K. Xu, J. Ba, R. Kiros, K. Cho, A.C. Courville, R. Salakhutdinov, R.S. Zemel, Y. Bengio, Show, attend and tell: Neural image caption generation with visual attention, in: F.R. Bach, D.M. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6–11 July 2015, Vol. 37 of JMLR Workshop and Conference Proceedings, JMLR.org, 2015, pp. 2048–2057. URL: http://proceedings.mlr.press/v37/xuc15.html.
  26. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need, in: I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S.V.N. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, 2017, pp. 5998–6008. URL: https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html.
  27. A. Veit, M.J. Wilber, S.J. Belongie, Residual networks behave like ensembles of relatively shallow networks, in: D.D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, R. Garnett (Eds.), Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5–10, 2016, Barcelona, Spain, 2016, pp. 550–558. URL: https://proceedings.neurips.cc/paper/2016/hash/37bc2f75bf1bcfe8450a1a41c200364c-Abstract.html.
  28. T. Chen, T. Moreau, Z. Jiang, L. Zheng, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. Ceze, et al., {TVM}) An automated end-to-end optimizing compiler for deep learning, in: 13th {USENIX})Symposium on Operating Systems Design and Implementation ({OSDI})18), 2018, pp. 578–594.
  29. Z. Liu, H. Tang, Y. Lin, S. Han, Point-voxel CNN for efficient 3d deep learning, in: H.M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E.B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8–14, 2019, Vancouver, BC, Canada, 2019, pp. 963–973. URL: https://proceedings.neurips.cc/paper/2019/hash/5737034557ef5b8c02c0e46513b98f90-Abstract.html.
  30. I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A.C. Courville, Y. Bengio, Generative adversarial nets, in: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8–13 2014, Montreal, Quebec, Canada, 2014, pp. 2672–2680. URL: https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html.
  31. Y. Wang, Z. Jiang, X. Chen, P. Xu, Y. Zhao, Y. Lin, Z. Wang, E2-train: Training state-of-the-art cnns with over 80% energy savings, in: H.M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E.B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8–14, 2019, Vancouver, BC, Canada, 2019, pp. 5139–5151. URL: https://proceedings.neurips.cc/paper/2019/hash/663772ea088360f95bac3dc7ffb841be-Abstract.html.
  32. Pan, 2009, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, 22, 1345, 10.1109/TKDE.2009.191 · DOI · Paper page
  33. Dosovitskiy, 2021, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, International Conference on Learning Representations (ICLR)
  34. Luo, 2021, HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search, Design, Automation \& Test in Europe Conference \& Exhibition (DATE)
  35. Luo, 2021, Designing Efficient DNNs via Hardware-Aware Neural Architecture Search and Beyond, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)
  36. Kong, 2022, HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs, Asia and South Pacific Design Automation Conference (ASP-DAC)
  37. Huai, 2021, ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems, ACM/IEEE Design Automation Conference (DAC)

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Metadata

Authors
Liu, D., Kong, H., Luo, X., Liu, W., & Subramaniam, R.
Affiliations
  • Nanyang Technological University (SG)
  • Hewlett-Packard (United States) (US)
Corresponding author
Luo, Xiangzhong
ORCID
Venue
Neurocomputing
Publisher
Elsevier BV
Published
2022-05
Volume
485
Pages
297-320
ISSN
0925-2312 (ISSN-L)
DOI
10.1016/j.neucom.2021.04.141
OpenAlex
W3110617657
Cited by
171
References
278
Citations by year
2026: 17 · 2025: 51 · 2024: 40 · 2023: 31 · 2022: 24
FWCI
8.95
Type
journal article
Language
en
Open access
Licence
Locations
  • Neurocomputing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Nanyang Technological University
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
edge AI, edge computing, deep learning, lightweight models, model compression, neural architecture search, hardware-aware neural architecture search, adaptive deep learning, edge devices

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