The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Open accessAbstract
Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks (DBNs) represent the state-of-the-art for many machine learning and computer vision classification problems. To overcome the large computational cost of deep networks, spiking deep networks have recently been proposed, given the specialized hardware now available for spiking neural networks (SNNs). However, this has come at the cost of performance losses due to the conversion from analog neural networks (ANNs) without a notion of time, to sparsely firing, event-driven SNNs. Here we analyze the effects of converting deep ANNs into SNNs with respect to the choice of parameters for spiking neurons such as firing rates and thresholds. We present a set of optimization techniques to minimize performance loss in the conversion process for ConvNets and fully connected deep networks. These techniques yield networks that outperform all previous SNNs on the MNIST database to date, and many networks here are close to maximum performance after only 20 ms of simulated time. The techniques include using rectified linear units (ReLUs) with zero bias during training, and using a new weight normalization method to help regulate firing rates. Our method for converting an ANN into an SNN enables low-latency classification with high accuracies already after the first output spike, and compared with previous SNN approaches it yields improved performance without increased training time. The presented analysis and optimization techniques boost the value of spiking deep networks as an attractive framework for neuromorphic computing platforms aimed at fast and efficient pattern recognition.
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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
Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0655-z · Cited by 2,289
Towards artificial general intelligence with hybrid Tianjic chip architecture
Pei, J., Deng, L., Song, S., Zhao, M., Zhang, Y., Wu, S., Wang, G., Zou, Z., Wu, Z., He, W., et al. (2019) · Nature · doi:10.1038/s41586-019-1424-8 · Cited by 1,082
Moradi, S., Qiao, N., Stefanini, F., & Indiveri, G. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2759700 · Cited by 650
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., & Benosman, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00186 · Cited by 486
Metadata
- Authors
- Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S.-C., & Pfeiffer, M.
- Venue
- 2015 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 1-8
- OpenAlex
- W1645800954
- Cited by
- 991
- References
- 32
- FWCI
- 70.38
- Type
- conference paper
- Language
- en
- Open access
- green
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- Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S.-C., & Pfeiffer, M. (2015). Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. 2015 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2015.7280696
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