2022 Roadmap on Neuromorphic Computing and Engineering
Abstract
Modern computation based on the von Neumann architecture is today 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. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83
Cite
@article{arxiv.2105.05956,
title = {2022 Roadmap on Neuromorphic Computing and Engineering},
author = {Dennis V. Christensen and Regina Dittmann and Bernabé Linares-Barranco and Abu Sebastian and Manuel Le Gallo and Andrea Redaelli and Stefan Slesazeck and Thomas Mikolajick and Sabina Spiga and Stephan Menzel and Ilia Valov and Gianluca Milano and Carlo Ricciardi and Shi-Jun Liang and Feng Miao and Mario Lanza and Tyler J. Quill and Scott T. Keene and Alberto Salleo and Julie Grollier and Danijela Marković and Alice Mizrahi and Peng Yao and J. Joshua Yang and Giacomo Indiveri and John Paul Strachan and Suman Datta and Elisa Vianello and Alexandre Valentian and Johannes Feldmann and Xuan Li and Wolfram H. P. Pernice and Harish Bhaskaran and Steve Furber and Emre Neftci and Franz Scherr and Wolfgang Maass and Srikanth Ramaswamy and Jonathan Tapson and Priyadarshini Panda and Youngeun Kim and Gouhei Tanaka and Simon Thorpe and Chiara Bartolozzi and Thomas A. Cleland and Christoph Posch and Shih-Chii Liu and Gabriella Panuccio and Mufti Mahmud and Arnab Neelim Mazumder and Morteza Hosseini and Tinoosh Mohsenin and Elisa Donati and Silvia Tolu and Roberto Galeazzi and Martin Ejsing Christensen and Sune Holm and Daniele Ielmini and N. Pryds},
journal= {arXiv preprint arXiv:2105.05956},
year = {2022}
}