Spoken vowel classification using synchronization of phase transition nano-oscillators

S. Dutta, A. Khanna, W. Chakraborty, J. Gomez, S. Joshi, S. Datta

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The paradigm of biologically-inspired computing endows the components of a neural network with dynamical functionality, such as self-oscillations, and harnesses emergent physical phenomena like synchronization, to learn and classify complex temporal patterns. In this work, we exploit the synchronization dynamics of a network of ultra-compact, low power Vanadium dioxide (VO2) based insulator-to-metal phase-transition nano-oscillators (IMT-NO) to classify complex temporal pattern for speech discrimination. We successfully train a network of four capacitively coupled IMT-NOs to recognize spoken vowels by tuning their oscillation frequencies electrically according to a real-time learning rule and achieve high recognition rates of 90.5% for spoken vowels. Such an energy-efficient compact hardware with a small number of functional elements are a promising technology option for edge artificial intelligence.

Original languageEnglish
Title of host publication2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesT128-T129
ISBN (Electronic)9784863487178
DOIs
StatePublished - Jun 2019
Externally publishedYes
Event39th Symposium on VLSI Technology, VLSI Technology 2019 - Kyoto, Japan
Duration: 9 Jun 201914 Jun 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
Country/TerritoryJapan
CityKyoto
Period9/06/1914/06/19

Bibliographical note

Publisher Copyright:
© 2019 The Japan Society of Applied Physics.

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