2022 roadmap on neuromorphic computing and engineering

神经形态工程学 冯·诺依曼建筑 计算机科学 计算机体系结构 非常规计算 边缘计算 技术路线图 大数据 功率消耗 建筑 国家(计算机科学) 人工智能 数据科学 GSM演进的增强数据速率 分布式计算 功率(物理) 人工神经网络 艺术 物理 算法 营销 量子力学 业务 视觉艺术 操作系统
作者
Dennis Valbjørn Christensen,Regina Dittmann,B. Linares-Barranco,Abu Sebastian,Manuel Le Gallo,A. Redaelli,Stefan Slesazeck,Thomas Mikolajick,Sabina Spiga,Stephan Menzel,Ilia Valov,Gianluca Milano,Carlo Ricciardi,Shi‐Jun Liang,Feng Miao,Mario Lanza,Tyler J. Quill,Scott T. Keene,Alberto Salleo,Julie Grollier,Danijela Marković,Alice Mizrahi,Peng Yao,J. Joshua Yang,Giacomo Indiveri,John Paul Strachan,Suman Datta,E. Vianello,Alexandre Valentian,Johannes Feldmann,Xuan Li,Wolfram H. P. Pernice,Harish Bhaskaran,Steve Furber,Emre Neftci,Franz Scherr,Wolfgang Maass,Srikanth Ramaswamy,Jonathan Tapson,Priyadarshini Panda,Youngeun Kim,Gouhei Tanaka,Simon J. Thorpe,Chiara Bartolozzi,Thomas A. Cleland,C. Posch,Shih-Chii Liu,Gabriella Panuccio,Murni Mahmud,Arnab Neelim Mazumder,Morteza Hosseini,Tinoosh Mohsenin,Elisa Donati,Silvia Tolu,Roberto Galeazzi,Martin Ejsing Christensen,Sune Holm,Daniele Ielmini,Nini Pryds
出处
期刊:Neuromorphic computing and engineering [IOP Publishing]
卷期号:2 (2): 022501-022501 被引量:180
标识
DOI:10.1088/2634-4386/ac4a83
摘要

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
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