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适应性读取偏差的建议:在铁电基神经形态系统中克服功率和缩放限制的解决方案

Ryun-Han Koo¹, Wonjun Shin¹, Seungwhan Kim¹

¹Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|December 1, 2023

概括

铁电道连接 (FTJ) 对神经形态计算具有前景,但受到噪音诱导的变化影响. 适应性读偏差方案显著降低了功耗,并提高了这些系统的可扩展性.

科学领域:

材料科学材料科学材料科学
计算机工程计算机工程
人工智能的人工智能

背景情况:

硬件神经形态系统需要节能数据处理.
氧化铁电道连接 (FTJ) 对人工突触器件来说是有前途的.
FTJ表现出非理想的特征,导致突触重量变化,阻碍神经形态系统的性能.

研究的目的:

分析基于FTJ的神经形态系统变化的起源和影响.
确定限制高性能神经形态计算的主要瓶.
提出一种新的方案,以克服高效的神经形态系统的设备限制.

主要方法:

在FTJ中研究的内在设备噪声 (1/f噪声)
分析了设备面积和读取偏差 (VRead) 对噪声水平的影响.
开发并实施了一个自适应性读取偏差 (ARB) 方案,具有层特定的VRead.

主要成果:

由1/f噪声驱动的动态变化是高性能神经形态系统的主要瓶.
减少设备面积和较低的VRead加剧了FTJ内在噪声.
与传统方法相比,ARB方案实现了显著的节能 (61.3%),并提高了可扩展性 (91.9%).

结论:

在FTJ中的内在1/f噪声对面积和功率高效的神经形态系统构成挑战.
拟议的ARB方案有效地减轻了噪音引起的变化.
ARB提高了神经形态系统的准确性,效率和可扩展性,为先进的AI硬件铺平了道路.

关键词:

铁电机电机电机电机电机电机氧化甲氧化物氧化甲低频噪声是一种低频噪声.这是一个神经形态神经形态的神经形态.具有高功率效率的高功率效率电池.

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