2022 roadmap on neuromorphic computing and engineering

Author:

Christensen Dennis VORCID,Dittmann Regina,Linares-Barranco Bernabe,Sebastian AbuORCID,Le Gallo ManuelORCID,Redaelli Andrea,Slesazeck StefanORCID,Mikolajick ThomasORCID,Spiga SabinaORCID,Menzel Stephan,Valov IliaORCID,Milano GianlucaORCID,Ricciardi CarloORCID,Liang Shi-JunORCID,Miao FengORCID,Lanza MarioORCID,Quill Tyler J,Keene Scott TORCID,Salleo Alberto,Grollier Julie,Marković Danijela,Mizrahi AliceORCID,Yao Peng,Yang J JoshuaORCID,Indiveri GiacomoORCID,Strachan John PaulORCID,Datta Suman,Vianello ElisaORCID,Valentian Alexandre,Feldmann Johannes,Li Xuan,Pernice Wolfram H P,Bhaskaran Harish,Furber Steve,Neftci Emre,Scherr Franz,Maass Wolfgang,Ramaswamy SrikanthORCID,Tapson Jonathan,Panda Priyadarshini,Kim Youngeun,Tanaka Gouhei,Thorpe Simon,Bartolozzi Chiara,Cleland Thomas A,Posch Christoph,Liu ShihChii,Panuccio GabriellaORCID,Mahmud MuftiORCID,Mazumder Arnab Neelim,Hosseini Morteza,Mohsenin Tinoosh,Donati ElisaORCID,Tolu SilviaORCID,Galeazzi Roberto,Christensen Martin Ejsing,Holm Sune,Ielmini DanieleORCID,Pryds NORCID

Abstract

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.

Funder

Japan Society for the Promotion of Science

Det Frie Forskningsråd

New Energy and Industrial Technology Development Organization

ETH Zürich Foundation

Defense Advanced Research Projects Agency

Villum Fonden

National Natural Science Foundation of China

Horizon 2020 Framework Programme

FP7 Information and Communication Technologies

Engineering and Physical Sciences Research Council

National Science Foundation

H2020 Future and Emerging Technologies

Collaborative Innovation Center of Advanced Microstructures

Helmholtz-Gemeinschaft

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Bundesministerium für Bildung und Forschung

Centre National de la Recherche Scientifique

Freistaat Sachsen

Deutsche Forschungsgemeinschaft

National Institute on Deafness and Other Communication Disorders

Publisher

IOP Publishing

Subject

General Medicine

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