2022 roadmap on neuromorphic computing and engineering

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...

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Published inNeuromorphic computing and engineering Vol. 2; no. 2; pp. 22501 - 22612
Main Authors Christensen, Dennis V, Dittmann, Regina, Linares-Barranco, Bernabe, Sebastian, Abu, Le Gallo, Manuel, Redaelli, Andrea, Slesazeck, Stefan, Mikolajick, Thomas, Spiga, Sabina, Menzel, Stephan, Valov, Ilia, Milano, Gianluca, Ricciardi, Carlo, Liang, Shi-Jun, Miao, Feng, Lanza, Mario, Quill, Tyler J, Keene, Scott T, Salleo, Alberto, Grollier, Julie, Marković, Danijela, Mizrahi, Alice, Yao, Peng, Yang, J Joshua, Indiveri, Giacomo, Strachan, John Paul, Datta, Suman, Vianello, Elisa, Valentian, Alexandre, Feldmann, Johannes, Li, Xuan, Pernice, Wolfram H P, Bhaskaran, Harish, Furber, Steve, Neftci, Emre, Scherr, Franz, Maass, Wolfgang, Ramaswamy, Srikanth, Tapson, Jonathan, Panda, Priyadarshini, Kim, Youngeun, Tanaka, Gouhei, Thorpe, Simon, Bartolozzi, Chiara, Cleland, Thomas A, Posch, Christoph, Liu, ShihChii, Panuccio, Gabriella, Mahmud, Mufti, Mazumder, Arnab Neelim, Hosseini, Morteza, Mohsenin, Tinoosh, Donati, Elisa, Tolu, Silvia, Galeazzi, Roberto, Christensen, Martin Ejsing, Holm, Sune, Ielmini, Daniele, Pryds, N
Format Journal Article
LanguageEnglish
Published IOP Publishing 01.06.2022
IOPScience
Subjects
Online AccessGet full text
ISSN2634-4386
2634-4386
DOI10.1088/2634-4386/ac4a83

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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.
AbstractList 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.
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.
Author Bartolozzi, Chiara
Menzel, Stephan
Valentian, Alexandre
Le Gallo, Manuel
Thorpe, Simon
Redaelli, Andrea
Yang, J Joshua
Scherr, Franz
Keene, Scott T
Christensen, Martin Ejsing
Dittmann, Regina
Li, Xuan
Milano, Gianluca
Panuccio, Gabriella
Sebastian, Abu
Bhaskaran, Harish
Mizrahi, Alice
Donati, Elisa
Spiga, Sabina
Liang, Shi-Jun
Liu, ShihChii
Pryds, N
Neftci, Emre
Ramaswamy, Srikanth
Posch, Christoph
Quill, Tyler J
Tanaka, Gouhei
Slesazeck, Stefan
Indiveri, Giacomo
Tolu, Silvia
Grollier, Julie
Cleland, Thomas A
Panda, Priyadarshini
Furber, Steve
Holm, Sune
Maass, Wolfgang
Mahmud, Mufti
Tapson, Jonathan
Hosseini, Morteza
Galeazzi, Roberto
Feldmann, Johannes
Pernice, Wolfram H P
Mohsenin, Tinoosh
Mazumder, Arnab Neelim
Salleo, Alberto
Yao, Peng
Kim, Youngeun
Datta, Suman
Lanza, Mario
Mikolajick, Thomas
Miao, Feng
Ielmini, Daniele
Christensen, Dennis V
Valov, Ilia
Ricciardi, Carlo
Vianello, Elisa
Strachan, John Paul
Linares-Barranco, Bernabe
Marković, Danijela
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Issue 2
Keywords deep learning
neuromorphic computation
memristor
spiking neural networks
robotics
convolutional neural networks
self-driving cars
Language English
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Snippet Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are...
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iop
SourceType Open Access Repository
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StartPage 22501
SubjectTerms Cognitive science
Computer science
convolutional neural networks
deep learning
memristor
neuromorphic computation
Neuroscience
robotics
self-driving cars
spiking neural networks
Title 2022 roadmap on neuromorphic computing and engineering
URI https://iopscience.iop.org/article/10.1088/2634-4386/ac4a83
https://hal.science/hal-03872100
Volume 2
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