2022 roadmap on neuromorphic computing and engineering


Change log
Authors
Dittmann, R 
Linares-Barranco, B 
Abstract

jats:titleAbstract</jats:title> jats:pModern 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 10jats:sup18</jats:sup> 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.</jats:p>

Description

Funder: ETH Zürich Foundation; doi: https://doi.org/10.13039/501100012652


Funder: Defense Advanced Research Projects Agency; doi: https://doi.org/10.13039/100000185


Funder: Collaborative Innovation Center of Advanced Microstructures; doi: https://doi.org/10.13039/501100016018


Funder: Bundesministerium für Bildung und Forschung; doi: https://doi.org/10.13039/501100002347


Funder: Centre National de la Recherche Scientifique; doi: https://doi.org/10.13039/501100004794


Funder: Freistaat Sachsen; doi: https://doi.org/10.13039/501100014913


Funder: Deutsche Forschungsgemeinschaft; doi: https://doi.org/10.13039/501100001659


Funder: National Institute on Deafness and Other Communication Disorders; doi: https://doi.org/10.13039/100000055

Keywords
neuromorphic computation, spiking neural networks, robotics, memristor, convolutional neural networks, self-driving cars, deep learning
Journal Title
Neuromorphic Computing and Engineering
Conference Name
Journal ISSN
2634-4386
2634-4386
Volume Title
2
Publisher
IOP Publishing
Sponsorship
Engineering and Physical Sciences Research Council (EP/M015130/1)