nceNCEECNNeuromorphic Computing and EngineeringNCENeuromorph. Comput. Eng.2634-4386IOP Publishingnceac4a8310.1088/2634-4386/ac4a83ac4a83NCE-100024.R1Roadmap2022 roadmap on neuromorphic computing and engineering0000-0003-0048-7595ChristensenDennis V1DittmannRegina2Linares-BarrancoBernabe30000-0001-5603-5243SebastianAbu40000-0003-1600-6151Le GalloManuel4RedaelliAndrea50000-0002-0414-0321SlesazeckStefan60000-0003-3814-0378MikolajickThomas670000-0001-7293-7503SpigaSabina8MenzelStephan90000-0002-0728-7214ValovIlia90000-0002-1983-6516MilanoGianluca100000-0002-4703-7949RicciardiCarlo110000-0002-3466-8063LiangShi-Jun120000-0002-0962-5424MiaoFeng120000-0003-4756-8632LanzaMario13QuillTyler J140000-0002-6635-670XKeeneScott T15SalleoAlberto14GrollierJulie16MarkovićDanijela160000-0003-2043-049XMizrahiAlice16YaoPeng170000-0001-8242-7531YangJ Joshua170000-0002-7109-1689IndiveriGiacomo180000-0002-5718-7924StrachanJohn Paul19DattaSuman200000-0002-8868-9951VianelloElisa21ValentianAlexandre22FeldmannJohannes1LiXuan23PerniceWolfram H P2425BhaskaranHarish23FurberSteve26NeftciEmre27ScherrFranz28MaassWolfgang280000-0001-6642-7136RamaswamySrikanth29TapsonJonathan30PandaPriyadarshini31KimYoungeun31TanakaGouhei32ThorpeSimon33BartolozziChiara34ClelandThomas A35PoschChristoph36LiuShihChii180000-0002-4059-7193PanuccioGabriella370000-0002-2037-8348MahmudMufti38MazumderArnab Neelim39HosseiniMorteza39MohseninTinoosh390000-0002-8091-1298DonatiElisa180000-0003-1825-8440ToluSilvia14GaleazziRoberto40ChristensenMartin Ejsing41HolmSune420000-0002-1853-1614IelminiDaniele430000-0002-5718-7924PrydsN1nipr@dtu.dk Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark Peter Gruenberg Institute 7, Forschungszentrum Juelich GmbH, 52425 Juelich, Germany and JARA-FIT, RWTH Aachen University, 52056 Aachen, Germany Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, 41092 Seville, Spain IBM Research - Zurich, Switzerland STMicroelectronics, Agrate, Italy NaMLab gGmbH, 01187 Dresden, Germany Institute of Semiconductors and Microsystems, TU Dresden; Dresden, Germany CNR-IMM, Unit of Agrate Brianza, Via C. Olivetti 2, Agrate Brianza (MB), Italy FZ Juelich (PGI-7), Juelich, Germany Advanced Materials Metrology and Life Science Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Torino, Italy Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, People’s Republic of China Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Saudi Arabia Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, United States of America Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767 Palaiseau, France Electrical and Computer Engineering Department, University of Southern California, Los Angeles, CA, United States of America Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland Hewlett Packard Laboratories, Hewlett Packard Enterprise, San Jose, CA, United States of America Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, United States of America CEA, LETI, Université Grenoble Alpes, Grenoble, France CEA, LIST, Université Grenoble Alpes, Grenoble, France Department of Materials, University of Oxford, Parks Road, OX1 3PH Oxford, United Kingdom Institute of Physics, University of Münster, Heisenbergstr. 11, 48149 Münster, Heisenbergstr., Germany Center for Soft Nanoscience, University of Münster, 48149 Münster, Germany The University of Manchester, United Kingdom Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States of America Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria École Polytechnique Fédérale de Lausanne, Geneva, Switzerland School of Electrical and Data Engineering, University of Technology, Sydney, Australia Department of Electrical Engineering, New Haven, Yale University, United States of America International Research Center for Neurointelligence (IRCN), The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-0033, Japan CerCo, Université Toulouse 3, CNRS, CHU Purpan, Pavillon Baudot, 31059 Toulouse, France Event Driven Perception for Robotics, Italian Institute of Technology, iCub Facility, Genoa, Italy Dept. of Psychology, Cornell University, Ithaca, NY, United States of America Prophesee, Paris, France Enhanced Regenerative Medicine, Istituto Italiano di Tecnologia, Italy Department of Computer Science and Medical Technologies Innovation Facility, Nottingham Trent University, United Kingdom University of Maryland, Baltimore County, Catonsville, United States of America Technical University of Denmark, Denmark The Danish Council on Ethics, Denmark Department of Food and Resource Economics, University of Copenhagen, Denmark Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano and IU.NET, 20133 Milano, Italy

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01620223052022205202222022501145202123102021121202225102021© 2022 The Author(s). Published by IOP Publishing Ltd2022 Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.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 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.

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