2022 roadmap on neuromorphic computing and engineering

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Dennis Valbjørn Christensen - (Autor:in)
  • Regina Dittmann - (Autor:in)
  • Bernabe Linares-Barranco - (Autor:in)
  • Abu Sebastian - (Autor:in)
  • Manuel Le Gallo - (Autor:in)
  • Andrea Redaelli - (Autor:in)
  • Stefan Slesazeck - (Autor:in)
  • Thomas Mikolajick - , Professur für Nanoelektronik (Autor:in)
  • Sabina Spiga - (Autor:in)
  • Stephan Menzel - (Autor:in)
  • Ilia Valov - (Autor:in)
  • Gianluca Milano - (Autor:in)
  • Carlo Ricciardi - (Autor:in)
  • Shi-Jun Liang - (Autor:in)
  • Feng Miao - (Autor:in)
  • Mario Lanza - (Autor:in)
  • Tyler J. Quill - (Autor:in)
  • Scott Tom Keene - (Autor:in)
  • Alberto Salleo - (Autor:in)
  • Julie Grollier - (Autor:in)
  • Danijela Markovic - (Autor:in)
  • Alice Mizrahi - (Autor:in)
  • Peng Yao - (Autor:in)
  • J. Joshua Yang - (Autor:in)
  • Giacomo Indiveri - (Autor:in)
  • John Paul Strachan - (Autor:in)
  • Suman Datta - (Autor:in)
  • Elisa Vianello - (Autor:in)
  • Alexandre Valentian - (Autor:in)
  • Johannes Feldmann - (Autor:in)
  • Xuan Li - (Autor:in)
  • Wolfram HP Pernice - (Autor:in)
  • Harish Bhaskaran - (Autor:in)
  • Steve Furber - (Autor:in)
  • Emre Neftci - (Autor:in)
  • Franz Scherr - (Autor:in)
  • Wolfgang Maass - (Autor:in)
  • Srikanth Ramaswamy - (Autor:in)
  • Jonathan Tapson - (Autor:in)
  • Priyadarshini Panda - (Autor:in)
  • Youngeun Kim - (Autor:in)
  • Gouhei Tanaka - (Autor:in)
  • Simon Thorpe - (Autor:in)
  • Chiara Bartolozzi - (Autor:in)
  • Thomas A Cleland - (Autor:in)
  • Christoph Posch - (Autor:in)
  • Shih-Chii Liu - (Autor:in)
  • Gabriella Panuccio - (Autor:in)
  • Mufti Mahmud - (Autor:in)
  • Arnab Neelim Mazumder - (Autor:in)
  • Morteza Hosseini - (Autor:in)
  • Tinoosh Mohsenin - (Autor:in)
  • Elisa Donati - (Autor:in)
  • Silvia Tolu - (Autor:in)
  • Roberto Galeazzi - (Autor:in)
  • Martin Ejsing Christensen - (Autor:in)
  • Sune Holm - (Autor:in)
  • Daniele Ielmini - (Autor:in)
  • Nini Pryds - (Autor:in)

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.

Details

OriginalspracheEnglisch
Aufsatznummer022501
Fachzeitschrift Neuromorphic computing and engineering
Jahrgang2
Ausgabenummer2
PublikationsstatusVeröffentlicht - 20 Mai 2022
Peer-Review-StatusJa

Externe IDs

Mendeley f57b3356-af9d-3c4c-bea2-19381c1d22a9
unpaywall 10.1088/2634-4386/ac4a83
Scopus 85148326290

Schlagworte

Schlagwörter

  • convolutional neural networks, deep learning, memristor, neuromorphic computation, robotics, self-driving cars, spiking neural networks

Bibliotheksschlagworte