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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 ( IF 4.6 ) Pub Date : 2021-11-16 , DOI: 10.1109/jetcas.2021.3128756
Stefania Carapezzi , Gabriele Boschetto , Corentin Delacour , Elisabetta Corti , Andrew Plews , Ahmed Nejim , Siegfried Karg , Aida Todri-Sanial

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.

中文翻译:

从材料和器件到用于边缘计算的仿脑振荡神经网络电路的先进设计方法

在本文中,我们评估了使用振荡器模拟生物神经元的创新概念,以使用基于二氧化钒 (VO 2 )。ONN 可以作为一种超低功耗的神经形态架构,能够执行关联记忆任务,例如物联网边缘设备中的模式识别。为了探索超 CMOS ONN 的收益和成本,需要建模、模拟和设计方法,从材料(例如原子方法)到设备(例如技术计算机辅助设计、TCAD)再到电路(例如混合-模式模拟,紧凑建模)。在这项工作中,我们报告了这种先进设计工具箱的开发以及超越 CMOS ONN 的性能和特性的结果。提议的设计工具箱允许探索模式识别应用程序的 ONN 可扩展性、准确性、能量和性能。
更新日期:2021-12-14
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