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 neuromor...

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Autores principales:	Julian Büchel, Dmitrii Zendrikov, Sergio Solinas, Giacomo Indiveri, Dylan R. Muir
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/14e9f78b35774441b084b0d57ac11b34
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spelling	oai:doaj.org-article:14e9f78b35774441b084b0d57ac11b342021-12-05T12:14:43ZSupervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors10.1038/s41598-021-02779-x2045-2322https://doaj.org/article/14e9f78b35774441b084b0d57ac11b342021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02779-xhttps://doaj.org/toc/2045-2322Abstract 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 temporal 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.Julian BüchelDmitrii ZendrikovSergio SolinasGiacomo IndiveriDylan R. MuirNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Julian Büchel Dmitrii Zendrikov Sergio Solinas Giacomo Indiveri Dylan R. Muir Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
description	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 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 temporal 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.
format	article
author	Julian Büchel Dmitrii Zendrikov Sergio Solinas Giacomo Indiveri Dylan R. Muir
author_facet	Julian Büchel Dmitrii Zendrikov Sergio Solinas Giacomo Indiveri Dylan R. Muir
author_sort	Julian Büchel
title	Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
title_short	Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
title_full	Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
title_fullStr	Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
title_full_unstemmed	Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
title_sort	supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/14e9f78b35774441b084b0d57ac11b34
work_keys_str_mv	AT julianbuchel supervisedtrainingofspikingneuralnetworksforrobustdeploymentonmixedsignalneuromorphicprocessors AT dmitriizendrikov supervisedtrainingofspikingneuralnetworksforrobustdeploymentonmixedsignalneuromorphicprocessors AT sergiosolinas supervisedtrainingofspikingneuralnetworksforrobustdeploymentonmixedsignalneuromorphicprocessors AT giacomoindiveri supervisedtrainingofspikingneuralnetworksforrobustdeploymentonmixedsignalneuromorphicprocessors AT dylanrmuir supervisedtrainingofspikingneuralnetworksforrobustdeploymentonmixedsignalneuromorphicprocessors
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Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors

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