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Advanced Design Methods From Materials and Devices to Circuits for Brain-Inspired Oscillatory Neural Networks for Edge Computing

Abstract : In this paper, we assess an innovative concept of emulating biological neurons with oscillators to implement an oscillatory neural network (ONN) with beyond-CMOS devices based on vanadium dioxide (VO2). ONNs can be of interest as an ultra-low-power neuromorphic architecture capable of performing associative memory tasks, such as pattern recognition in IoT edge devices. To explore the benefits and costs of beyond-CMOS ONNs necessitates modeling, simulation, and design methods spanning from materials (e.g., atomistic methods) to devices (e.g., technology-computer-aided-design, TCAD) up to circuits (e.g., mixed-mode simulation, compact modeling). In this work, we report on the development of such an advanced design toolbox and the results on performance and features of beyond-CMOS ONNs. The proposed design toolbox allows exploring ONN scalability, accuracy, energy, and performance for pattern recognition applications.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03231504
Contributor : Stefania Carapezzi <>
Submitted on : Thursday, May 20, 2021 - 6:42:33 PM
Last modification on : Tuesday, May 25, 2021 - 9:16:00 AM

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  • HAL Id : lirmm-03231504, version 1

Citation

Stefania Carapezzi, Gabriele Boschetto, Corentin Delacour, Elisabetta Corti, Andrew Plews, et al.. Advanced Design Methods From Materials and Devices to Circuits for Brain-Inspired Oscillatory Neural Networks for Edge Computing. 2021. ⟨lirmm-03231504⟩

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