TY - JOUR
T1 - Advanced Design Methods From Materials and Devices to Circuits for Brain-Inspired Oscillatory Neural Networks for Edge Computing
AU - Carapezzi, Stefania
AU - Boschetto, Gabriele
AU - Delacour, Corentin
AU - Corti, Elisabetta
AU - Plews, Andrew
AU - Nejim, Ahmed
AU - Karg, Siegfried
AU - Todri-Sanial, Aida
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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 (VO 2 ). 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.
AB - 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 (VO 2 ). 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.
KW - Oscillators
KW - Integrated circuit modeling
KW - Computational modeling
KW - Orbits
KW - Performance evaluation
KW - Semiconductor device modeling
KW - Discrete Fourier transforms
KW - Oscillatory neural networks (ONN)
KW - beyond-CMOS devices
KW - vanadium dioxide
KW - density functional theory (DFT)
KW - technology computer-aided design (TCAD)
KW - compact modeling
KW - circuit simulation
KW - Internet-of-Things (IoT)
KW - edge artificial intelligence (edge AI)
KW - neuromorphic computing
KW - associative memory
UR - https://www.scopus.com/pages/publications/85121770320
U2 - 10.1109/JETCAS.2021.3128756
DO - 10.1109/JETCAS.2021.3128756
M3 - Article
SN - 2156-3357
VL - 11
SP - 586
EP - 596
JO - IEEE Journal on Emerging and Selected Topics in Circuits and Systems
JF - IEEE Journal on Emerging and Selected Topics in Circuits and Systems
IS - 4
M1 - 4
ER -