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Journal Articles Neuromorphic Computing and Engineering Year : 2022

2022 roadmap on neuromorphic computing and engineering

Abu Sebastian
Manuel Le Gallo
Stefan Slesazeck
Sabina Spiga
Stephan Menzel
  • Function : Author
Ilia Valov
Shi-Jun Liang
Feng Miao
Tyler Quill
  • Function : Author
Scott Keene
J Joshua Yang
Suman Datta
  • Function : Author
Elisa Vianello
Alexandre Valentian
  • Function : Author
Johannes Feldmann
  • Function : Author
Xuan Li
  • Function : Author
Wolfram Pernice
  • Function : Author
Harish Bhaskaran
  • Function : Author
Steve Furber
  • Function : Author
Emre Neftci
  • Function : Author
Franz Scherr
  • Function : Author
Wolfgang Maass
  • Function : Author
Srikanth Ramaswamy
Jonathan Tapson
  • Function : Author
Priyadarshini Panda
  • Function : Author
Youngeun Kim
  • Function : Author
Gouhei Tanaka
  • Function : Author
Simon Thorpe
Chiara Bartolozzi
  • Function : Author
Thomas Cleland
  • Function : Author
Christoph Posch
  • Function : Author
Shihchii Liu
  • Function : Author
Gabriella Panuccio
Mufti Mahmud
Arnab Neelim Mazumder
  • Function : Author
Morteza Hosseini
  • Function : Author
Tinoosh Mohsenin
  • Function : Author
Elisa Donati
Silvia Tolu
Roberto Galeazzi
  • Function : Author
Martin Ejsing Christensen
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Sune Holm
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Daniele Ielmini
N Pryds
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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 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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hal-03872100 , version 1 (24-11-2022)
hal-03872100 , version 2 (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-03872100v2⟩
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