Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors

Abstract

Mixed-signal analog/digital circuits can 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 therefore 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 addresses this challenge by maximizing 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