Abstract
“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 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.”

Dennis Valbjørn Christensen1, Regina Dittmann2, Bernabe Linares-Barranco3, Abu Sebastian4, Manuel Le Gallo5, Andrea Redaelli6, Stefan Slesazeck7, Thomas Mikolajick8, Sabina Spiga9, Stephan Menzel10, Ilia Valov11, Gianluca Milano12, Carlo Ricciardi13, Shi-Jun Liang14, Feng Miao15, Mario Lanza16, Tyler J. Quill17, Scott Tom Keene18, Alberto Salleo17, Julie Grollier19, Danijela Markovic20, Alice Mizrahi20, Peng Yao21, J. Joshua Yang22, Giacomo Indiveri23, John Paul Strachan24, Suman Datta25, Elisa Vianello26, Alexandre Valentian27, Johannes Feldmann28, Xuan Li28, Wolfram HP Pernice29, Harish Bhaskaran28, Steve Furber30, Emre Neftci31, Franz Scherr32, Wolfgang Maass33, Srikanth Ramaswamy34, Jonathan Tapson35, Priyadarshini Panda36, Youngeun Kim36, Gouhei Tanaka37, Simon Thorpe38, Chiara Bartolozzi39, Thomas A Cleland40, Christoph Posch41, Shih-Chii Liu23, Gabriella Panuccio42, Mufti Mahmud43, Arnab Neelim Mazumder44, Morteza Hosseini44, Tinoosh Mohsenin45, Elisa Donati23, Silvia Tolu46, Roberto Galeazzi47, Martin Ejsing Christensen48, Sune Holm49, Daniele Ielmini50 and Nini Pryds51. “2022 roadmap on neuromorphic computing and engineering.” Neuromorphic Computing and Engineering (2022).