Spoken vowel classification using synchronization of phase transition nano-oscillators

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers
EditorialInstitute of Electrical and Electronics Engineers Inc.
PáginasT128-T129
ISBN (versión digital)9784863487178
DOI
EstadoPublicada - jun. 2019
Publicado de forma externa
Evento39th Symposium on VLSI Technology, VLSI Technology 2019 - Kyoto, Japón
Duración: 9 jun. 201914 jun. 2019

Serie de la publicación

NombreDigest of Technical Papers - Symposium on VLSI Technology
Volumen2019-June
ISSN (versión impresa)0743-1562

Conferencia

Conferencia39th Symposium on VLSI Technology, VLSI Technology 2019
País/TerritorioJapón
CiudadKyoto
Período9/06/1914/06/19

Nota bibliográfica

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

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