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

ABSTRACT: 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

SUBMITTER: Buchel J 

PROVIDER: S-EPMC8642544 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature