2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernab\'e Linares-Barranco,, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas, Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo, Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza

TL;DR
This paper provides a comprehensive overview of the current state, challenges, and future directions of neuromorphic computing, emphasizing its potential to revolutionize low-power, brain-inspired computation and edge applications.
Contribution
It offers a collective perspective from leading researchers on neuromorphic technology, covering materials, devices, circuits, algorithms, applications, and ethical considerations.
Findings
Neuromorphic systems can significantly reduce power consumption compared to traditional architectures.
Current challenges include developing suitable materials and scalable devices.
Future opportunities involve edge computing and autonomous systems.
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…
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