The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Convolutional networks for fast, energy-efficient neuromorphic computing
Open accessAbstract
Significance: Brain-inspired computing seeks to develop new technologies that solve real-world problems while remaining grounded in the physical requirements of energy, speed, and size. Meeting these challenges requires high-performing algorithms that are capable of running on efficient hardware. Here, we adapt deep convolutional neural networks, which are today’s state-of-the-art approach for machine perception in many domains, to perform classification tasks on neuromorphic hardware, which is today’s most efficient platform for running neural networks. Using our approach, we demonstrate near state-of-the-art accuracy on eight datasets, while running at between 1,200 and 2,600 frames/s and using between 25 and 275 mW.
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Cites
ImageNet 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
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Learning representations by back-propagating errors
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Backpropagation Applied to Handwritten Zip Code Recognition
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Neuromorphic electronic systems
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Cited by
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
“1Note: Recent work using TrueNorth in a stylized fashion allows it to be used to compute reduced precision neural networks [9].”
MethodDeep learning with coherent nanophotonic circuits
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., et al. (2017) · Nature Photonics · doi:10.1038/nphoton.2017.93 · Cited by 3,340
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Self-citationEIE: efficient inference engine on compressed deep neural network
Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M. A., & Dally, W. J. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001163 · Cited by 1,937
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Metadata
- Authors
- Esser, S. K., Merolla, P. A., Arthur, J. V., Cassidy, A. S., Appuswamy, R., Andreopoulos, A., Berg, D. J., McKinstry, J. L., Melano, T., Barch, D. R., di Nolfo, C., Datta, P., Amir, A., Taba, B., Flickner, M. D., & Modha, D. S.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2016-09-20
- Volume
- 113
- Issue
- 41
- Pages
- 11441-11446
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2314470091
- Cited by
- 781
- References
- 12
- FWCI
- 46.19
- Type
- journal article
- Language
- en
- Open access
- green
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- Esser, S. K., Merolla, P. A., Arthur, J. V., Cassidy, A. S., Appuswamy, R., Andreopoulos, A., Berg, D. J., McKinstry, J. L., Melano, T., Barch, D. R., di Nolfo, C., Datta, P., Amir, A., Taba, B., Flickner, M. D., & Modha, D. S. (2016). Convolutional networks for fast, energy-efficient neuromorphic computing. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1604850113
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