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摘要

随着深度学习的高速发展, 目前智能算法的飞速更新迭代对硬件算力提出了很高的要求. 受限于摩尔定律的告竭以及冯·诺伊曼瓶颈, 传统CMOS集成无法满足硬件算力提升的迫切需求. 利用新型器件忆阻器构建神经形态计算系统可以实现存算一体, 拥有极高的并行度和超低功耗的特点, 被认为是解决传统计算机架构瓶颈的有效途径, 受到了全世界的广泛关注. 本文按照自下而上的顺序, 首先综述了主流忆阻器的器件结构、物理机理, 并比较分析了它们的性能特性. 然后, 介绍了近年来忆阻器实现人工神经元和人工突触的进展, 包括具体的电路形式和神经形态功能的模拟. 接着, 综述了无源和有源忆阻阵列的结构形式以及它们在神经形态计算中的应用, 具体包括基于神经网络的手写数字和人脸识别等. 最后总结了目前从底层到顶层所遇到的挑战, 并对该领域后续的发展进行了展望.

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Abstract

With the rapid development of deep learning, the current rapid update and iteration of intelligent algorithms put forward high requirements for hardware computing power. Limited by the exhaustion of Moore’s law and the von Neumann bottleneck, the traditional CMOS integration cannot meet the urgent needs of hardware computing power improvement. The utilization of new device memristors to construct a neuromorphic computing system can realize the integration of storage and computing, and has the characteristics of extremely high parallelism and ultra-low power consumption. In this work, the device structure and physical mechanism of mainstream memristors are reviewed in bottom-to-top order firstly, and their performance characteristics are compared and analyzed. Then, the recent research progress of memristors to realize artificial neurons and artificial synapses is introduced, including the simulation of specific circuit forms and neuromorphic functions. Secondly, in this work, the structural forms of passive and active memristive arrays and their applications in neuromorphic computing, including neural network-based handwritten digits and face recognition, are reviewed. Lastly, the current challenges of memristive brain-like computing from the bottom to the top, are summarized and the future development of this field is also prospected.

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作者及机构信息

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通信作者:何毓辉,heyuhui@hust.edu.cn; 缪向水,miaoxs@hust.edu.cn

基金项目:国家重点研发计划(批准号: 2019YFB2205100)和国家自然科学基金(批准号: 92064012, 61974051, 61874164, 51732003)资助的课题

Authors and contacts

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Corresponding author:He Yu-Hui,heyuhui@hust.edu.cn; Miao Xiang-Shui,miaoxs@hust.edu.cn

Funds:Project supported by the National Key R&D Program of China (Grant No. 2019YFB2205100) and the National Natural Science Foundation of China (Grant Nos. 92064012, 61974051, 61874164, 51732003)

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施引文献

下载: 全尺寸图片幻灯片

参数指标	RRAM	PCRAM	MRAM	FeRAM
切换速度/ps	85^[43]	700^[54]	210^[85]	4000^[86]
开关比	10⁷^[87]	10⁶^[88]	< 10^[67,89]	10⁷^[84]
室温保持特性/a	> 1000^[44]	> 10^[90]	> 10^[91]	> 10^[76,81]
耐擦写次数	10¹²^[45]	10¹¹^[92]	5 × 10¹⁴^[66]	10¹¹^[82]
开关功耗/fJ	115^[42]	50^[93]	10^[94]	10^[95]

下载: 导出CSV

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