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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 pp 36–56Cite as

Neural Architecture Search for Spiking Neural Networks

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13684)

Abstract

Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence processing of binary information in SNNs. To address this, in this paper, we introduce a novel Neural Architecture Search (NAS) approach for finding better SNN architectures. Inspired by recent NAS approaches that find the optimal architecture from activation patterns at initialization, we select the architecture that can represent diverse spike activation patterns across different data samples without training. Moreover, to further leverage the temporal information among the spikes, we search for feed-forward connections as well as backward connections (i.e., temporal feedback connections) between layers. Interestingly, SNASNet found by our search algorithm achieves higher performance with backward connections, demonstrating the importance of designing SNN architecture for suitably using temporal information. We conduct extensive experiments on three image recognition benchmarks where we show that SNASNet achieves state-of-the-art performance with significantly lower timesteps (5 timesteps). Code is available on Github.

Keywords

  • Spiking Neural Networks
  • Neural architecture search
  • Neuromorphic computing

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Acknowledgment

This work was supported in part by C-BRIC, a JUMP center sponsored by DARPA and SRC, Google Research Scholar Award, the National Science Foundation (Grant#1947826), TII (Abu Dhabi) and the DARPA AI Exploration (AIE) program.

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  1. Department of Electrical Engineering, Yale University, New Haven, CT, USA

    Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha & Priyadarshini Panda

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  1. Youngeun Kim
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  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Kim, Y., Li, Y., Park, H., Venkatesha, Y., Panda, P. (2022). Neural Architecture Search for Spiking Neural Networks. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13684. Springer, Cham. https://doi.org/10.1007/978-3-031-20053-3_3

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