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.

本论文研究了Metropolis-Hastings采样在训练受强烈未知非理想化影响的脉冲神经网络（SNN）硬件中的应用，并将所提出的方法与在文献中广泛使用的误差反向传播（backprop）算法和替代梯度进行了比较。通过在芯片连环训练环境中进行模拟，该研究旨在训练一个受未知失真影响的SNN来进行癌症检测。结果表明，当硬件非理想化程度较高时，所提出的方法的准确性要比backprop高达27%。此外，结果还表明，为了达到有效的准确性，所提出的方法在SNN泛化方面也优于backprop，所需的训练数据量要少于10倍。这些发现使得所提出的训练方法非常适用于模拟亚阈值电路和其他新兴技术中的SNN实现，其中未知硬件非理想化可能危及backprop算法的性能。

基于 Metropolis-Hastings 采样的芯片内脉冲神经网络训练方法研究