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 in | Neuromorphic computing and engineering Vol. 2; no. 2; pp. 22501 - 22612 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
IOP Publishing
01.06.2022
IOPScience |
Subjects | |
Online Access | Get full text |
ISSN | 2634-4386 2634-4386 |
DOI | 10.1088/2634-4386/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. |
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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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Lyngby, Denmark – sequence: 2 givenname: Regina surname: Dittmann fullname: Dittmann, Regina organization: RWTH Aachen University Peter Gruenberg Institute 7, Forschungszentrum Juelich GmbH, 52425 Juelich, Germany and JARA-FIT, 52056 Aachen, Germany – sequence: 3 givenname: Bernabe surname: Linares-Barranco fullname: Linares-Barranco, Bernabe organization: CSIC and Universidad de Sevilla Instituto de Microelectrónica de Sevilla (IMSE-CNM), 41092 Seville, Spain – sequence: 4 givenname: Abu orcidid: 0000-0001-5603-5243 surname: Sebastian fullname: Sebastian, Abu organization: IBM Research - Zurich , Switzerland – sequence: 5 givenname: Manuel orcidid: 0000-0003-1600-6151 surname: Le Gallo fullname: Le Gallo, Manuel organization: IBM Research - Zurich , Switzerland – sequence: 6 givenname: Andrea surname: Redaelli fullname: Redaelli, Andrea organization: STMicroelectronics , Agrate, Italy – sequence: 7 givenname: Stefan orcidid: 0000-0002-0414-0321 surname: Slesazeck fullname: Slesazeck, Stefan organization: NaMLab gGmbH , 01187 Dresden, Germany – sequence: 8 givenname: Thomas orcidid: 0000-0003-3814-0378 surname: Mikolajick fullname: Mikolajick, Thomas organization: Institute of Semiconductors and Microsystems , TU Dresden; Dresden, Germany – sequence: 9 givenname: Sabina orcidid: 0000-0001-7293-7503 surname: Spiga fullname: Spiga, Sabina organization: Unit of Agrate Brianza CNR-IMM, Via C. 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SubjectTerms | Cognitive science Computer science convolutional neural networks deep learning memristor neuromorphic computation Neuroscience robotics self-driving cars spiking neural networks |
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