The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification
Open accessAbstract
neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.
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Cites
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Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Opportunities for neuromorphic computing algorithms and applications
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Training Spiking Neural Networks Using Lessons From Deep Learning
Eshraghian, J. K., Ward, M., Neftci, E. O., Wang, X., Lenz, G., Dwivedi, G., Bennamoun, M., Jeong, D. S., & Lu, W. D. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3308088 · Cited by 782
Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook
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Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Kim, S., Park, S., Na, B., & Yoon, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6787 · Cited by 451
Metadata
- Authors
- Rueckauer, B., Lungu, I.-A., Hu, Y., Pfeiffer, M., & Liu, S.-C.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2017-12-07
- Volume
- 11
- Pages
- 682-682
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2775079417
- Cited by
- 1,072
- References
- 52
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- gold
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- Rueckauer, B., Lungu, I.-A., Hu, Y., Pfeiffer, M., & Liu, S.-C. (2017). Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification. Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2017.00682
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