Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling

• URL: http://arxiv.org/abs/2402.06284v1
• Date: Fri, 9 Feb 2024 09:49:05 GMT
• Title: Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling
• Authors: Ali Safa, Vikrant Jaltare, Samira Sebt, Kameron Gano, Johannes Leugering, Georges Gielen, Gert Cauwenberghs
• Abstract summary: This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities. Our results show that the proposed approach strongly outperforms the use of backprop by up to $27%$ higher accuracy when subject to strong hardware non-idealities.
• Score: 0.9025833922570009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities, and compares the proposed approach to the common use of the backpropagation of error (backprop) algorithm and surrogate gradients, widely used to train SNNs in literature. Simulations are conducted within a chip-in-the-loop training context, where an SNN subject to unknown distortion must be trained to detect cancer from measurements, within a biomedical application context. Our results show that the proposed approach strongly outperforms the use of backprop by up to $27\%$ higher accuracy when subject to strong hardware non-idealities. Furthermore, our results also show that the proposed approach outperforms backprop in terms of SNN generalization, needing $>10 \times$ less training data for achieving effective accuracy. These findings make the proposed training approach well-suited for SNN implementations in analog subthreshold circuits and other emerging technologies where unknown hardware non-idealities can jeopardize backprop.

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