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.

翻译：本文研究了在存在强未知非理想性时，利用Metropolis-Hastings采样训练脉冲神经网络(SNN)硬件的方法，并将所提方法与文献中广泛用于训练SNN的误差反向传播(backprop)算法及替代梯度方法进行了对比。在芯片在环训练场景下开展仿真实验，其中受未知失真影响的SNN需在生物医学应用背景下根据检测数据识别癌症。结果表明，在硬件存在强非理想性时，所提方法的精度较反向传播算法提升高达27%。此外，研究进一步显示，所提方法在SNN泛化能力方面优于反向传播算法，达到有效精度所需的训练数据量减少超过10倍。这些发现使所提训练方法特别适用于模拟亚阈值电路及其他新兴技术中的SNN实现——在这些场景中，未知硬件非理想性可能严重影响反向传播算法的性能。