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 IMTNOs 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 original | Inglés |
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Título de la publicación alojada | 2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers |
Editorial | Institute of Electrical and Electronics Engineers Inc. |
Páginas | T128-T129 |
ISBN (versión digital) | 9784863487185 |
DOI | |
Estado | Publicada - jun. 2019 |
Publicado de forma externa | Sí |
Evento | 33rd Symposium on VLSI Circuits, VLSI Circuits 2019 - Kyoto, Japón Duración: 9 jun. 2019 → 14 jun. 2019 |
Serie de la publicación
Nombre | IEEE Symposium on VLSI Circuits, Digest of Technical Papers |
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Volumen | 2019-June |
Conferencia
Conferencia | 33rd Symposium on VLSI Circuits, VLSI Circuits 2019 |
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País/Territorio | Japón |
Ciudad | Kyoto |
Período | 9/06/19 → 14/06/19 |
Nota bibliográfica
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