Analog Synaptic Behaviors in Carbon-Based Self-Selective RRAM for In-Memory Supervised Learning

Ying‐Chen Chen, J. Eshraghian, Isaiah Shipley, Maxwell Weiss
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引用次数: 1

Abstract

New computational paradigms are required to overcome the von-Neumann bottleneck by reducing main memory access. Neuromorphic and in-memory computing has brought on much promise for improving efficiency in a subset of tasks, and emerging memory technologies are inextricably tied to localized memory accesses. However, the sneak path current (SPC) through unselected neighboring cells is a major challenge occurring in high density storage application in the crossbar array configuration. In this work, carbon-based self-selective memory is shown to overcome the SPC problem and additionally is demonstrated to be a potential candidate as a nanodevice for resource-constrained in-memory supervised learning, by taking advantage of its analog synaptic behaviors. Device variation and non-idealities are characterized in the context of neural network regularization, in fulfilling the aim to reduce the ever-increasing power demands of modern computing.
基于碳基自选择RRAM的记忆监督学习模拟突触行为
需要新的计算范式来克服冯-诺伊曼瓶颈,减少主存储器的访问。神经形态和内存计算为提高任务子集的效率带来了很多希望,新兴的内存技术与局部内存访问密不可分。然而,通过未选择的相邻单元的潜行路径电流(SPC)是在交叉棒阵列配置中高密度存储应用中出现的主要挑战。在这项工作中,碳基自选择记忆被证明克服了SPC问题,并且通过利用其模拟突触行为被证明是一种潜在的候选纳米器件,用于资源受限的内存监督学习。在神经网络正则化的背景下,设备变化和非理想性被表征,以实现降低现代计算不断增长的功率需求的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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