记忆电阻器技术加速了大脑启发的计算

Chenchen Liu, Fuqiang Liu, Hai Li
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引用次数: 2

摘要

以大脑为灵感的计算,被称为神经形态计算,已经显示出巨大的潜力,可以为计算带来革命性的高效率。在神经形态引擎中,在单个芯片上实现了巨大的计算和功率效率。然而,基于传统纳米技术的神经形态设计进展缓慢。忆阻器技术的出现和应用,将神经形态计算的发展推向了一个新的时代。神经网络中最基本的矩阵-向量乘法运算可以通过忆阻交叉棒自然高效地实现。近年来,各种神经形态系统被广泛地应用于认知和感知领域。本文从器件、电路、系统、算法等方面综述了该领域的发展现状。对挑战和未来进行了研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain-inspired computing accelerated by memristor technology
The brain-inspired computing, known as neuromorphic computing has demonstrated great potential in revolutionizing computation for high efficiency. In the neuromorphic engine, tremendous computing and power efficiency are achieved on a single chip. However, the development progress is slow in the neuromorphic designs based on conventional nanotechnologies. The occurrence and utilization of memristor technology pushed the development of neuromorphic computing forward into a new era. Matrix-vector multiplication, which is the basic computation in the neural network can be implemented by the memristor crossbar naturally and efficiently. Recently, various neuromorphic systems have been widely developed for cognition and perception applications. In this work, the development status is reviewed from the aspects of device, circuit, system, and algorithm. The challenges and futures are studied.
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