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2022 roadmap on neuromorphic computing and engineering

Neuromorphic computing and engineering, 2022-06, Vol.2 (2), p.22501 [Peer Reviewed Journal]

2022 The Author(s). Published by IOP Publishing Ltd ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 2634-4386 ;EISSN: 2634-4386 ;DOI: 10.1088/2634-4386/ac4a83 ;CODEN: NCEECN

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Title:
2022 roadmap on neuromorphic computing and engineering
Author: Christensen, Dennis V ; Dittmann, Regina ; Linares-Barranco, Bernabe ; Sebastian, Abu ; Le Gallo, Manuel ; Redaelli, Andrea ; Slesazeck, Stefan ; Mikolajick, Thomas ; Spiga, Sabina ; Menzel, Stephan ; Valov, Ilia ; Milano, Gianluca ; Ricciardi, Carlo ; Liang, Shi-Jun ; Miao, Feng ; Lanza, Mario ; Quill, Tyler J ; Keene, Scott T ; Salleo, Alberto ; Grollier, Julie ; Marković, Danijela ; Mizrahi, Alice ; Yao, Peng ; Yang, J Joshua ; Indiveri, Giacomo ; Strachan, John Paul ; Datta, Suman ; Vianello, Elisa ; Valentian, Alexandre ; Feldmann, Johannes ; Li, Xuan ; Pernice, Wolfram H P ; Bhaskaran, Harish ; Furber, Steve ; Neftci, Emre ; Scherr, Franz ; Maass, Wolfgang ; Ramaswamy, Srikanth ; Tapson, Jonathan ; Panda, Priyadarshini ; Kim, Youngeun ; Tanaka, Gouhei ; Thorpe, Simon ; Bartolozzi, Chiara ; Cleland, Thomas A ; Posch, Christoph ; Liu, ShihChii ; Panuccio, Gabriella ; Mahmud, Mufti ; Mazumder, Arnab Neelim ; Hosseini, Morteza ; Mohsenin, Tinoosh ; Donati, Elisa ; Tolu, Silvia ; Galeazzi, Roberto ; Christensen, Martin Ejsing ; Holm, Sune ; Ielmini, Daniele ; Pryds, N
Subjects: convolutional neural networks ; deep learning ; Engineering Sciences ; memristor ; neuromorphic computation ; Physics ; robotics ; self-driving cars ; spiking neural networks
Is Part Of: Neuromorphic computing and engineering, 2022-06, Vol.2 (2), p.22501
Description: Abstract Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10 18 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.
Publisher: IOP Publishing
Language: English
Identifier: ISSN: 2634-4386
EISSN: 2634-4386
DOI: 10.1088/2634-4386/ac4a83
CODEN: NCEECN
Source: IOP Publishing Free Content
IOPscience (Open Access)
DOAJ Directory of Open Access Journals

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2022 roadmap on neuromorphic computing and engineering

2022 The Author(s). Published by IOP Publishing Ltd ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 2634-4386 ;EISSN: 2634-4386 ;DOI: 10.1088/2634-4386/ac4a83 ;CODEN: NCEECN

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