2022 Roadmap on Neuromorphic Computing and Engineering
Creators: name: Christensen, Dennis V.
name: Dittmann, Regina
name: Linares-Barranco, Bernabé
name: Sebastian, Abu
name: Gallo, Manuel Le
name: Redaelli, Andrea
name: Slesazeck, Stefan
name: Mikolajick, Thomas
name: Spiga, Sabina
name: Menzel, Stephan
name: Valov, Ilia
name: Milano, Gianluca
name: Ricciardi, Carlo
name: Liang, Shi-Jun
name: Miao, Feng
name: Lanza, Mario
name: Quill, Tyler J.
name: Keene, Scott T.
name: Salleo, Alberto
name: Grollier, Julie
name: Marković, Danijela
name: Mizrahi, Alice
name: Yao, Peng
name: Yang, J. Joshua
name: Indiveri, Giacomo
name: Strachan, John Paul
name: Datta, Suman
name: Vianello, Elisa
name: Valentian, Alexandre
name: Feldmann, Johannes
name: Li, Xuan
name: Pernice, Wolfram H. P.
name: Bhaskaran, Harish
name: Furber, Steve
name: Neftci, Emre
name: Scherr, Franz
name: Maass, Wolfgang
name: Ramaswamy, Srikanth
name: Tapson, Jonathan
name: Panda, Priyadarshini
name: Kim, Youngeun
name: Tanaka, Gouhei
name: Thorpe, Simon
name: Bartolozzi, Chiara
name: Cleland, Thomas A.
name: Posch, Christoph
name: Liu, Shih-Chii
name: Panuccio, Gabriella
name: Mahmud, Mufti
name: Mazumder, Arnab Neelim
name: Hosseini, Morteza
name: Mohsenin, Tinoosh
name: Donati, Elisa
name: Tolu, Silvia
name: Galeazzi, Roberto
name: Christensen, Martin Ejsing
name: Holm, Sune
name: Ielmini, Daniele
name: Pryds, N.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Emerging Technologies (cs.ET)
subject: Disordered Systems and Neural Networks (cond-mat.dis-nn)
subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: Modern computation based on the von Neumann architecture is today 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 1018 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. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83