2022 roadmap on neuromorphic computing and engineering
2022 roadmap on neuromorphic computing and engineering
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DOI：
10.1088/2634-4386/ac4a83
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发表时间：
2022-06-01
期刊：
影响因子：
--
通讯作者：
Pryds, N.
中科院分区：
文献类型：
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作者：
Christensen, Dennis, V;Dittmann, Regina;Pryds, N.
基于冯·诺伊曼体系结构的现代计算现在是一门成熟的前沿科学。在冯·诺伊曼体系结构中，处理和存储单元被实现为密集且连续地交换数据的单独块。这种数据传输在很大程度上消耗了电力。下一代计算机技术有望以每秒1018次计算的速度解决艾级级的问题。即使这些未来的计算机将是令人难以置信的强大，如果它们是基于冯·诺伊曼类型的架构，它们将消耗20到30兆瓦的电力，并且不会像我们的大脑那样具有内在的物理内置能力来学习或处理复杂的数据。这些需求可以通过神经形态计算系统来解决，这些系统受到人脑生物学概念的启发。这一新一代计算机具有以比传统处理器低得多的功率消耗来存储和处理大量数字信息的潜力。在它们潜在的未来应用中，一个重要的利基市场是将控制权从数据中心转移到边缘设备。这份路线图的目的是展示神经形态技术的现状的快照，并就未来神经形态技术的主要领域，即材料、设备、神经形态电路、神经形态算法、应用和伦理的挑战和机遇提供意见。路线图是一个视角的集合，神经形态社区的主要研究人员就每个研究领域的现状和未来挑战提供了他们自己的观点。我们希望这份路线图将是一个有用的资源，为该领域以外的读者提供一个简洁而全面的介绍，供那些刚刚进入该领域的读者使用，并为那些在神经形态计算社区建立良好基础的人提供未来的前景。
Modern 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 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.