基于忆阻纳米器件的节能计算硬件

IF 2.3 Q3 NANOSCIENCE & NANOTECHNOLOGY

IEEE Nanotechnology Magazine Pub Date : 2023-10-01 DOI:10.1109/MNANO.2023.3297106

Y. Huang, Vignesh Ravichandran, Wuyu Zhao, Q. Xia

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引用次数: 0

摘要

计算硬件是影响我们日常生活的人工智能的关键驱动因素之一。然而，尽管近几十年来取得了重大进步，但为人工智能提供动力的计算硬件，尤其是深度神经网络的能耗仍然远高于人脑。基于新兴纳米器件（如忆阻器）的硬件创新为节能计算系统提供了潜在的解决方案。这篇综述讨论了开发基于忆阻纳米器件的节能计算硬件所面临的挑战，并总结了忆阻器件、交叉阵列、系统和算法方面的最新进展，旨在从自下而上的方法解决这些问题。提出了进一步提高未来计算硬件能效的潜在研究方向。

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Towards Energy-Efficient Computing Hardware Based on Memristive Nanodevices

Computing hardware is one of the crucial drivers of artificial intelligence (AI) that impacts our daily lives. However, despite the significant improvements made in recent decades, the energy consumption of computing hardware that powers AI, especially deep neural networks, remains considerably higher than that of human brains. Hardware innovations based on emerging nanodevices like memristors offer potential solutions to energy-efficient computing systems. This review discusses the challenges associated with developing energy-efficient computing hardware based on memristive nanodevices and summarizes recent progress in memristive devices, crossbar arrays, systems, and algorithms, aiming at addressing these issues from a bottom-up approach. Potential research directions are proposed to further improve future computing hardware's energy efficiency.

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来源期刊

IEEE Nanotechnology Magazine NANOSCIENCE & NANOTECHNOLOGY-

CiteScore

2.90

自引率

6.20%

发文量

期刊介绍： IEEE Nanotechnology Magazine publishes peer-reviewed articles that present emerging trends and practices in industrial electronics product research and development, key insights, and tutorial surveys in the field of interest to the member societies of the IEEE Nanotechnology Council. IEEE Nanotechnology Magazine will be limited to the scope of the Nanotechnology Council, which supports the theory, design, and development of nanotechnology and its scientific, engineering, and industrial applications.