  Mixed-signal analog/digital circuits emulate spiking neurons and synapses
with extremely high energy efficiency, an approach known as "neuromorphic
engineering". However, analog circuits are sensitive to process-induced
variation among transistors in a chip ("device mismatch"). For neuromorphic
implementation of Spiking Neural Networks (SNNs), mismatch causes parameter
variation between identically-configured neurons and synapses. Each chip
exhibits a different distribution of neural parameters, causing deployed
networks to respond differently between chips. Current solutions to mitigate
mismatch based on per-chip calibration or on-chip learning entail increased
design complexity, area and cost, making deployment of neuromorphic devices
expensive and difficult. Here we present a supervised learning approach that
produces SNNs with high robustness to mismatch and other common sources of
noise. Our method trains SNNs to perform temporal classification tasks by
mimicking a pre-trained dynamical system, using a local learning rule from
non-linear control theory. We demonstrate our method on two tasks requiring
memory, and measure the robustness of our approach to several forms of noise
and mismatch. We show that our approach is more robust than common alternatives
for training SNNs. Our method provides robust deployment of pre-trained
networks on mixed-signal neuromorphic hardware, without requiring per-device
training or calibration.
