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Lookup NU author(s): Dr Srikanth RamaswamyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2022 The Author(s). Published by IOP Publishing Ltd. 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 1018 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.
Author(s): Christensen DV, Dittmann R, Linares-Barranco B, Sebastian A, Le Gallo M, Redaelli A, Slesazeck S, Mikolajick T, Spiga S, Menzel S, Valov I, Milano G, Ricciardi C, Liang S-J, Miao F, Lanza M, Quill TJ, Keene ST, Salleo A, Grollier J, Markovic D, Mizrahi A, Yao P, Yang JJ, Indiveri G, Strachan JP, Datta S, Vianello E, Valentian A, Feldmann J, Li X, Pernice WHP, Bhaskaran H, Furber S, Neftci E, Scherr F, Maass W, Ramaswamy S, Tapson J, Panda P, Kim Y, Tanaka G, Thorpe S, Bartolozzi C, Cleland TA, Posch C, Liu S, Panuccio G, Mahmud M, Mazumder AN, Hosseini M, Mohsenin T, Donati E, Tolu S, Galeazzi R, Christensen ME, Holm S, Ielmini D, Pryds N
Publication type: Article
Publication status: Published
Journal: Neuromorphic Computing and Engineering
Year: 2022
Volume: 2
Issue: 2
Print publication date: 01/06/2022
Online publication date: 20/05/2022
Acceptance date: 12/01/2022
Date deposited: 05/03/2025
ISSN (electronic): 2634-4386
Publisher: Institute of Physics Publishing Ltd
URL: https://doi.org/10.1088/2634-4386/ac4a83
DOI: 10.1088/2634-4386/ac4a83
Data Access Statement: The data that support the findings of this study are available upon reasonable request from the authors.
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