Advertisement

Deep Spiking Neural Network: Energy Efficiency Through Time Based Coding

Conference paper
  • 807 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12355)

Abstract

Spiking Neural Networks (SNNs) are promising for enabling low-power event-driven data analytics. The best performing SNNs for image recognition tasks are obtained by converting a trained deep learning Analog Neural Network (ANN) composed of Rectified Linear Unit (ReLU) activation to SNN consisting of Integrate-and-Fire (IF) neurons with “proper” firing thresholds. However, this has come at the cost of accuracy loss and higher inference latency due to lack of a notion of time. In this work, we propose an ANN to SNN conversion methodology that uses a time-based coding scheme, named Temporal-Switch-Coding (TSC), and a corresponding TSC spiking neuron model. Each input image pixel is presented using two spikes and the timing between the two spiking instants is proportional to the pixel intensity. The real-valued ReLU activations in ANN are encoded using the spike-times of the TSC neurons in the converted TSC-SNN. At most two memory accesses and two addition operations are performed for each synapse during the whole inference, which significantly improves the SNN energy efficiency. We demonstrate the proposed TSC-SNN for VGG-16, ResNet-20 and ResNet-34 SNNs on datasets including CIFAR-10 (93.63% top-1), CIFAR-100 (70.97% top-1) and ImageNet (73.46% top-1 accuracy). It surpasses the best inference accuracy of the converted rate-encoded SNN with 7–14.5\(\times \) lesser inference latency, and 30–60\(\times \) fewer addition operations and memory accesses per inference across datasets.

Keywords

Spiking Neural Network ANN-SNN conversion Temporal coding Energy efficiency Deep learning Machine learning 

Notes

Acknowledgment

This work was supported in part by Center for Brain-Inspired Computing (C-BRIC), a MARCO and DARPA sponsored StarNet center, by the Semiconductor Research Corporation, National Science Foundation, Sandia National Laboratories, Vannevar Bush Faculty Fellowship and by the US Army Research Laboratory and the UK Ministry of Defense under Agreement Number W911NF-16-3-0001.

References

  1. 1.
    Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W.: Long short-term memory and learning-to-learn in networks of spiking neurons. In: Advances in Neural Information Processing Systems, pp. 787–797. Montréal, Quebec, Canada (2018)Google Scholar
  2. 2.
    Blouw, P., Choo, X., Hunsberger, E., Eliasmith, C.: Benchmarking keyword spotting efficiency on neuromorphic hardware. In: Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop, p. 1. ACM (2019)Google Scholar
  3. 3.
    Cao, Y., Chen, Y., Khosla, D.: Spiking deep convolutional neural networks for energy-efficient object recognition. Int. J. Comput. Vis. 113(1), 54–66 (2015).  https://doi.org/10.1007/s11263-014-0788-3MathSciNetCrossRefGoogle Scholar
  4. 4.
    Davies, M., et al.: Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1), 82–99 (2018)CrossRefGoogle Scholar
  5. 5.
    Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015)CrossRefGoogle Scholar
  6. 6.
    Diehl, P.U., Neil, D., Binas, J., Cook, M., Liu, S.C., Pfeiffer, M.: Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2015)Google Scholar
  7. 7.
    Diehl, P.U., Zarrella, G., Cassidy, A., Pedroni, B.U., Neftci, E.: Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. In: 2016 IEEE International Conference on Rebooting Computing (ICRC), pp. 1–8. IEEE (2016)Google Scholar
  8. 8.
    Esser, S.K., et al.: Convolutional networks for fast, energy-efficient neuromorphic computing. CoRR abs/1603.08270 (2016). http://arxiv.org/abs/1603.08270
  9. 9.
    Ferré, P., Mamalet, F., Thorpe, S.J.: Unsupervised feature learning with winner-takes-all based STDP. Front. Comput. Neurosci. 12, 24 (2018)CrossRefGoogle Scholar
  10. 10.
    Han, B., Srinivasan, G., Roy, K.: RMP-SNN: residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020Google Scholar
  11. 11.
    Hardt, M., Ma, T.: Identity matters in deep learning. CoRR abs/1611.04231. http://arxiv.org/abs/1611.04231 (2016)
  12. 12.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385. http://arxiv.org/abs/1512.03385 (2015)
  13. 13.
    Heeger, D.: Poisson model of spike generation. Stanford Univ. Handout 5, 1–13 (2000)Google Scholar
  14. 14.
    Hunsberger, E., Eliasmith, C.: Training spiking deep networks for neuromorphic hardware. CoRR abs/1611.05141. http://arxiv.org/abs/1611.05141 (2016)
  15. 15.
    Jin, Y., Zhang, W., Li, P.: Hybrid macro/micro level backpropagation for training deep spiking neural networks. In: Advances in Neural Information Processing Systems, pp. 7005–7015. Montréal, Quebec, Canada (2018)Google Scholar
  16. 16.
    Johnson, M., et al.: Google’s multilingual neural machine translation system: enabling zero-shot translation. Trans. Assoc. Comput. Linguit. 5, 339–351 (2017)Google Scholar
  17. 17.
    Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T.: STDP-based spiking deep convolutional neural networks for object recognition. Neural Netw. 99, 56–67 (2018).  https://doi.org/10.1016/j.neunet.2017.12.005. http://www.sciencedirect.com/science/article/pii/S0893608017302903
  18. 18.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  19. 19.
    Lee, C., Srinivasan, G., Panda, P., Roy, K.: Deep spiking convolutional neural network trained with unsupervised spike timing dependent plasticity. IEEE Trans. Cogn. Dev. Syst. pp. 1–1 (2018).  https://doi.org/10.1109/TCDS.2018.2833071
  20. 20.
    Lee, C., Sarwar, S.S., Panda, P., Srinivasan, G., Roy, K.: Enabling spike-based backpropagation for training deep neural network architectures. Front. Neurosci. 14, 119 (2020).  https://doi.org/10.3389/fnins.2020.00119CrossRefGoogle Scholar
  21. 21.
    Lee, J.H., Delbruck, T., Pfeiffer, M.: Training deep spiking neural networks using backpropagation. Front. Neurosci. 10, 508 (2016)Google Scholar
  22. 22.
    Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659–1671 (1997)CrossRefGoogle Scholar
  23. 23.
    Masquelier, T., Thorpe, S.J.: Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput. Biol. 3(2), e31 (2007)CrossRefGoogle Scholar
  24. 24.
    Merolla, P.A., et al.: A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668–673 (2014)CrossRefGoogle Scholar
  25. 25.
    Miyashita, D., Lee, E.H., Murmann, B.: Convolutional neural networks using logarithmic data representation. CoRR abs/1603.01025. http://arxiv.org/abs/1603.01025 (2016)
  26. 26.
    Mozafari, M., Ganjtabesh, M., Nowzari-Dalini, A., Thorpe, S.J., Masquelier, T.: Combining STDP and reward-modulated STDP in deep convolutional spiking neural networks for digit recognition. arXiv preprint arXiv:1804.00227 (2018)
  27. 27.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)Google Scholar
  28. 28.
    Neftci, E.O., Mostafa, H., Zenke, F.: Surrogate gradient learning in spiking neural networks. arXiv preprint arXiv:1901.09948 (2019)
  29. 29.
    Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 689–696 (2011)Google Scholar
  30. 30.
    Panda, P., Roy, K.: Unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 299–306. IEEE, Vancouver, British Columbia, Canada (2016)Google Scholar
  31. 31.
    Pérez-Carrasco, J.A., et al.: Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate coding and coincidence processing-application to feedforward ConvNets. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2706–2719 (2013)CrossRefGoogle Scholar
  32. 32.
    Rathi, N., Srinivasan, G., Panda, P., Roy, K.: Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. In: International Conference on Learning Representations. https://openreview.net/forum?id=B1xSperKvH (2020)
  33. 33.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  34. 34.
    Rueckauer, B., Liu, S.: Conversion of analog to spiking neural networks using sparse temporal coding. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5 (2018)Google Scholar
  35. 35.
    Rueckauer, B., Lungu, I.A., Hu, Y., Pfeiffer, M.: Theory and tools for the conversion of analog to spiking convolutional neural networks. arXiv preprint arXiv:1612.04052 (2016)
  36. 36.
    Rueckauer, B., Lungu, I.A., Hu, Y., Pfeiffer, M., Liu, S.C.: Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Front. Neurosci. 11, 682 (2017)CrossRefGoogle Scholar
  37. 37.
    Sengupta, A., Ye, Y., Wang, R., Liu, C., Roy, K.: Going deeper in spiking neural networks: VGG and residual architectures. Front. Neurosci. 13, 95 (2019)CrossRefGoogle Scholar
  38. 38.
    Shrestha, S.B., Orchard, G.: Slayer: spike layer error reassignment in time. In: Advances in Neural Information Processing Systems, pp. 1412–1421. Montréal, Quebec, Canada (2018)Google Scholar
  39. 39.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)Google Scholar
  40. 40.
    Srinivasan, G., Panda, P., Roy, K.: STDP-based unsupervised feature learning using convolution-over-time in spiking neural networks for energy-efficient neuromorphic computing. J. Emerg. Technol. Comput. Syst. 14(4), 1–12 (2018).  https://doi.org/10.1145/3266229.  https://doi.org/10.1145/3266229
  41. 41.
    Srinivasan, G., Roy, K.: ReStoCNet: residual stochastic binary convolutional spiking neural network for memory-efficient neuromorphic computing. Front. Neurosci. 13, 189 (2019)CrossRefGoogle Scholar
  42. 42.
    Tavanaei, A., Kirby, Z., Maida, A.S.: Training spiking convnets by STDP and gradient descent. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. Rio de Janeiro, Brazil, July 2018.  https://doi.org/10.1109/IJCNN.2018.8489104
  43. 43.
    Thiele, J.C., Bichler, O., Dupret, A.: Event-based, timescale invariant unsupervised online deep learning with STDP. Front. Comput. Neurosci. 12, 46 (2018).  https://doi.org/10.3389/fncom.2018.00046. https://www.frontiersin.org/article/10.3389/fncom.2018.00046
  44. 44.
    Wu, Y., Deng, L., Li, G., Zhu, J., Shi, L.: Spatio-temporal backpropagation for training high-performance spiking neural networks. Front. Neurosci. 12, 331 (2018)CrossRefGoogle Scholar
  45. 45.
    Zambrano, D., Nusselder, R., Scholte, H.S., Bohte, S.M.: Efficient computation in adaptive artificial spiking neural networks. CoRR abs/1710.04838. http://arxiv.org/abs/1710.04838 (2017)
  46. 46.
    Zhang, M., Zheng, N., Ma, D., Pan, G., Gu, Z.: Efficient spiking neural networks with logarithmic temporal coding. CoRR abs/1811.04233. http://arxiv.org/abs/1811.04233 (2018)
  47. 47.
    Zhao, B., Ding, R., Chen, S., Linares-Barranco, B., Tang, H.: Feedforward categorization on AER motion events using cortex-like features in a spiking neural network. IEEE Trans. Neural Netw. Learn. Syst. 26(9), 1963–1978 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Purdue UniversityWest LafayetteUSA

Personalised recommendations