  Mixed-signal neuromorphic processors provide extremely low-power operation
for edge inference workloads, taking advantage of sparse asynchronous
computation within Spiking Neural Networks (SNNs). However, deploying robust
applications to these devices is complicated by limited controllability over
analog hardware parameters, as well as unintended parameter and dynamical
variations of analog circuits due to fabrication non-idealities. Here we
demonstrate a novel methodology for ofDine training and deployment of spiking
neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2.
The methodology utilizes gradient-based training using a differentiable
simulation of the mixed-signal device, coupled with an unsupervised weight
quantization method to optimize the network's parameters. Parameter noise
injection during training provides robustness to the effects of quantization
and device mismatch, making the method a promising candidate for real-world
applications under hardware constraints and non-idealities. This work extends
Rockpool, an open-source deep-learning library for SNNs, with support for
accurate simulation of mixed-signal SNN dynamics. Our approach simplifies the
development and deployment process for the neuromorphic community, making
mixed-signal neuromorphic processors more accessible to researchers and
developers.
