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

Stefania Carapezzi 1 Gabriele Boschetto 1 Corentin Delacour 1 Elisabetta Corti 2 Andrew Plews Ahmed Nejim Siegfried Karg 2 Aida Todri-Sanial 1 
1 SmartIES - Smart Integrated Electronic Systems
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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 Connect in order to contact the contributor
Submitted on : Thursday, May 20, 2021 - 6:42:33 PM
Last modification on : Friday, August 5, 2022 - 3:02:16 PM

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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. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2021, 11 (4), pp.586-596. ⟨10.1109/JETCAS.2021.3128756⟩. ⟨lirmm-03231504⟩

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