Roadmap on emerging hardware and technology for machine learning
Roadmap on emerging hardware and technology for machine learning
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DOI：
10.1088/1361-6528/aba70f
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发表时间：
2021-01-01
期刊：
影响因子：
3.5
通讯作者：
Raychowdhury, Arijit
中科院分区：
文献类型：
--
作者：
Berggren, Karl;Xia, Qiangfei;Raychowdhury, Arijit
人工智能近期的进展在很大程度上归因于机器学习的快速发展，尤其是在算法和神经网络模型方面。然而，是硬件的性能，特别是计算系统的能效，设定了机器学习能力的基本极限。以数据为中心的计算需要硬件系统的革命，因为基于晶体管和冯·诺依曼架构的传统数字计算机并非是为神经形态计算而专门设计的。一个基于新兴器件和新架构的硬件平台是未来计算在吞吐量和能效大幅提高方面的希望所在。然而，构建这样一个系统面临着诸多挑战，从材料选择、器件优化、电路制造到系统集成等等。本路线图的目的是呈现对机器学习可能有益的新兴硬件技术的概况，为《纳米技术》的读者提供这一新兴领域中挑战和机遇的视角。
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.