Abstract
Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true. In this work, we present a metric to estimate the energy consumption of SNNs independently of a specific hardware. We then apply this metric on SNNs processing three different data types (static, dynamic and event-based) representative of real-world applications. As a result, all of our SNNs are 6 to 8 times more efficient than their FNN counterparts.
This research is funded by the ANR project DeepSee, Université Côte d’Azur, CNRS and Région Sud Provence-Alpes-Côte d’Azur.
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References
Abderrahmane, N., Miramond, B., Kervennic, E., Girard, A.: Spleat: spiking low-power event-based architecture for in-orbit processing of satellite imagery. In: International Joint Conference on Neural Networks (2022)
Amir, A., et al.: A low power, fully event-based gesture recognition system. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7243–7252 (2017)
Barchid, S., Mennesson, J., Eshraghian, J., Djéraba, C., Bennamoun, M.: Spiking neural networks for frame-based and event-based single object localization (2022). https://doi.org/10.48550/ARXIV.2206.06506
Bardow, P., Davison, A.J., Leutenegger, S.: Simultaneous optical flow and intensity estimation from an event camera. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 884–892 (2016). https://doi.org/10.1109/CVPR.2016.102
Cordone, L., Miramond, B., Thierion, P.: Object detection with spiking neural networks on automotive event data. In: International Joint Conference on Neural Networks (2022)
Davidson, S., Furber, S.B.: Comparison of artificial and spiking neural networks on digital hardware. Front. Neurosci. 15, 651141 (2021)
Deng, L., et al.: Rethinking the performance comparison between SNNs and ANNs. Neural Netw. 121, 294–307 (2020)
Ding, J., Yu, Z., Tian, Y., Huang, T.: Optimal ANN-SNN conversion for fast and accurate inference in deep spiking neural networks. In: International Joint Conference on Artificial Intelligence, pp. 2328–2336 (2021). https://doi.org/10.24963/ijcai.2021/321
Fang, W., et al.: Spikingjelly (2020). https://github.com/fangwei123456/spikingjelly. Accessed 29 July 2022
Jouppi, N.P., et al.: Ten lessons from three generations shaped Google’s tpuv4i: industrial product. In: ACM/IEEE Annual International Symposium on Computer Architecture, pp. 1–14 (2021)
Khacef, L., Abderrahmane, N., Miramond, B.: Confronting machine-learning with neuroscience for neuromorphic architectures design. In: International Joint Conference on Neural Networks (2018). https://doi.org/10.1109/IJCNN.2018.8489241
Kheradpisheh, S.R., Masquelier, T.: Temporal backpropagation for spiking neural networks with one spike per neuron. Int. J. Neural Syst. 30(06), 2050027 (2020)
Kundu, S., Datta, G., Pedram, M., Beerel, P.A.: Spike-thrift: Towards energy-efficient deep spiking neural networks by limiting spiking activity via attention-guided compression. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3953–3962 (2021)
Lemaire, E.: Modélisation et exploration d’architectures neuromorphiques pour les systèmes embarqués haute-performance. Ph.D. thesis, Univ. Côte d’Azur (2022)
Lemaire, E., Miramond, B., Bilavarn, S., Saoud, H., Abderrahmane, N.: Synaptic activity and hardware footprint of spiking neural networks in digital neuromorphic systems. ACM Trans. Embed. Comput. Syst. (2022)
Neftci, E., Mostafa, H., Zenke, F.: Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Sig. Process. Mag. 36, 51–63 (2019). https://doi.org/10.1109/MSP.2019.2931595
Pellegrini, T., Zimmer, R., Masquelier, T.: Low-activity supervised convolutional spiking neural networks applied to speech commands recognition. In: 2021 IEEE Spoken Language Technology Workshop (SLT), pp. 97–103. IEEE (2021)
Rueckauer, B., et al.: NXTF: an API and compiler for deep spiking neural networks on intel Loihi (2021). https://doi.org/10.48550/ARXIV.2101.04261
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.: Hats: Histograms of averaged time surfaces for robust event-based object classification. In: IEEE Conference on Computer Vision and Pattern Recognition, June 2018
Zimmer, R., Pellegrini, T., Singh, S.F., Masquelier, T.: Technical report: supervised training of convolutional spiking neural networks with PyTorch (2019)
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Lemaire, E., Cordone, L., Castagnetti, A., Novac, PE., Courtois, J., Miramond, B. (2023). An Analytical Estimation of Spiking Neural Networks Energy Efficiency. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_48
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