Biologically Plausible Ferroelectric Quasi-Leaky Integrate and Fire Neuron

S. Dutta, A. Saha, P. Panda, W. Chakraborty, J. Gomez, A. Khanna, S. Gupta, K. Roy, S. Datta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Biologically plausible mechanism like homeostasis compliments Hebbian learning to allow unsupervised learning in spiking neural networks [1]. In this work, we propose a novel ferroelectric-based quasi-LIF neuron that induces intrinsic homeostasis. We experimentally characterize and perform phase-field simulations to delineate the non-trivial transient polarization relaxation mechanism associated with multi-domain interaction in poly-crystalline ferroelectric, such as Zr doped HfO2, that underlines the Q-LIF behavior. Network level simulations with the Q-LIF neuron model exhibits a 2.3x reduction in firing rate compared to traditional LIF neuron while maintaining iso-accuracy of 84-85% across varying network sizes. Such an energy-efficient hardware for spiking neuron can enable ultra-low power data processing in energy constrained environments suitable for edge-intelligence.

Original languageEnglish (US)
Title of host publication2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesT140-T141
ISBN (Electronic)9784863487178
DOIs
StatePublished - Jun 2019
Event39th Symposium on VLSI Technology, VLSI Technology 2019 - Kyoto, Japan
Duration: Jun 9 2019Jun 14 2019

Publication series

NameDigest of Technical Papers - Symposium on VLSI Technology
Volume2019-June
ISSN (Print)0743-1562

Conference

Conference39th Symposium on VLSI Technology, VLSI Technology 2019
CountryJapan
CityKyoto
Period6/9/196/14/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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