Rahimi Azghadi, Mostafa, Chen, Ying-Chen, Eshraghian, Jason K., Chen, Jia, Lin, Chih-Yang, Amirsoleimani, Amirali, Mehonic, Adnan, Kenyon, Anthony J., Fowler, Burt, Lee, Jack C., and Chang, Yao-Feng (2020) CMOS and memristive hardware for neuromorphic computing. Advanced Intelligent Systems, 2 (5). 1900189.

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution. Download (7MB) | Preview

Abstract

The ever-increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low power, high speed, and noise-tolerant computing capabilities of the brain, may provide such a shift. To that end, various aspects of the brain, from its basic building blocks, such as neurons and synapses, to its massively parallel in-memory computing networks have been being studied by the huge neuroscience community. Concurrently, many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop bio-inspired (neuromorphic) computing platforms.

Item ID:	62462
Item Type:	Article (Research - C1)
ISSN:	2640-4567
Copyright Information:	© 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Date Deposited:	24 Mar 2020 00:12
FoR Codes:	40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400908 Microelectronics @ 100%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 50% 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 50%
Downloads:	Total: 99 Last 12 Months: 13
	More Statistics