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Article Dans Une Revue Neuromorphic Computing and Engineering Année : 2022

2022 roadmap on neuromorphic computing and engineering

Abu Sebastian
Manuel Le Gallo
Stefan Slesazeck
Sabina Spiga
Stephan Menzel
  • Fonction : Auteur
Ilia Valov
Shi-Jun Liang
Feng Miao
Tyler Quill
  • Fonction : Auteur
Scott Keene
J Joshua Yang
Suman Datta
  • Fonction : Auteur
Elisa Vianello
Alexandre Valentian
  • Fonction : Auteur
Johannes Feldmann
  • Fonction : Auteur
Xuan Li
  • Fonction : Auteur
Wolfram Pernice
  • Fonction : Auteur
Harish Bhaskaran
  • Fonction : Auteur
Steve Furber
  • Fonction : Auteur
Emre Neftci
  • Fonction : Auteur
Franz Scherr
  • Fonction : Auteur
Wolfgang Maass
  • Fonction : Auteur
Srikanth Ramaswamy
Jonathan Tapson
  • Fonction : Auteur
Priyadarshini Panda
  • Fonction : Auteur
Youngeun Kim
  • Fonction : Auteur
Gouhei Tanaka
  • Fonction : Auteur
Simon Thorpe
Chiara Bartolozzi
  • Fonction : Auteur
Thomas Cleland
  • Fonction : Auteur
Christoph Posch
  • Fonction : Auteur
Shihchii Liu
  • Fonction : Auteur
Gabriella Panuccio
Mufti Mahmud
Arnab Neelim Mazumder
  • Fonction : Auteur
Morteza Hosseini
  • Fonction : Auteur
Tinoosh Mohsenin
  • Fonction : Auteur
Elisa Donati
Silvia Tolu
Roberto Galeazzi
  • Fonction : Auteur
Martin Ejsing Christensen
  • Fonction : Auteur
Sune Holm
  • Fonction : Auteur
Daniele Ielmini
N Pryds
  • Fonction : Auteur

Résumé

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.
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Dates et versions

hal-03872100 , version 1 (25-11-2022)

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Dennis Christensen, Regina Dittmann, Bernabe Linares-Barranco, Abu Sebastian, Manuel Le Gallo, et al.. 2022 roadmap on neuromorphic computing and engineering. Neuromorphic Computing and Engineering, 2022, 2 (2), pp.022501. ⟨10.1088/2634-4386/ac4a83⟩. ⟨hal-03872100⟩
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