STDG: Fast and Lightweight SNN Training Technique Using Spike Temporal Locality
Conference Publication ResearchOnline@JCUAbstract
Spiking neural networks (SNNs) possess biological plausibility and energy efficiency as they communicate using asynchronous and mostly sparse spikes. These features make them an ideal choice for efficient neuromorphic computing. The non-differentiable, discrete binary spike events transmitted in SNNs pose a challenge for applying gradient-based optimization algorithms directly to these networks. Therefore, efficient techniques are necessary to enhance energy efficiency without sacrificing accuracy. In this work, we propose Spike Timing Dependent Gradient (STDG), a fast and lightweight learning scheme that uses temporal locality among spikes to avoid non-differentiable derivatives. Our experiments show that STDG reaches the state-of-the-art accuracy of 99.5% and 98.2% on the Caltech101 face/motorbike and the MNIST datasets, respectively.
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Publication Name
BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings
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ISBN/ISSN
9798350300260
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Pages Count
5
Location
Toronto, Canada
Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
Piscataway, NJ, USA
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DOI
10.1109/BioCAS58349.2023.10388882