Towards Chip-in-the-loop Spiking Neural Network Training via
  Metropolis-Hastings Sampling
Ali Safa Vikrant Jaltare Samira Sebt Kameron Gano Johannes Leugering Georges Gielen Gert Cauwenberghs
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.