Study of Weight Quantization Associations over a Weight Range for Application in Memristor Devices.

IF 3 3区 工程技术 Q2 CHEMISTRY, ANALYTICAL
Micromachines Pub Date : 2024-10-15 DOI:10.3390/mi15101258
Yerim Kim, Hee Yeon Noh, Gyogwon Koo, Hyunki Lee, Sanghan Lee, Rock-Hyun Choi, Shinbuhm Lee, Myoung-Jae Lee, Hyeon-Jun Lee
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Abstract

The development of hardware-based cognitive computing systems critically hinges upon the integration of memristor devices capable of versatile weight expression across a spectrum of resistance levels while preserving consistent electrical properties. This investigation aims to explore the practical implementation of a digit recognition system utilizing memristor devices with minimized weighting levels. Through the process of weight quantization for digits represented by 25 or 49 input signals, the study endeavors to ascertain the feasibility of digit recognition via neural network computation. The integration of memristor devices into the system architecture is poised to streamline the representation of the resistors required for weight expression, thereby facilitating the realization of neural-network-based cognitive systems. To minimize the information corruption in the system caused by weight quantization, we introduce the concept of "weight range" in this work. The weight range is the range between the maximum and minimum values of the weights in the neural network. We found that this has a direct impact on weight quantization, which reduces the number of digits represented by a weight below a certain level. This was found to help maintain the information integrity of the entire system despite the reduction in weight levels. Moreover, to validate the efficacy of the proposed methodology, quantized weights are systematically applied to an array of double-layer neural networks. This validation process involves the construction of cross-point array circuits with dimensions of 25 × 10 and 10 × 10, followed by a meticulous examination of the resultant changes in the recognition rate of randomly generated numbers through device simulations. Such endeavors contribute to advancing the understanding and practical implementation of hardware-based cognitive computing systems leveraging memristor devices and weight quantization techniques.

研究权重范围内的权重量化关联,以应用于 Memristor 器件。
基于硬件的认知计算系统的开发,关键在于能否集成忆阻器器件,使其在保持一致的电气特性的同时,还能在各种电阻水平范围内实现多种权重表达。这项研究旨在探索如何利用权重最小化的忆阻器器件实际实现数字识别系统。通过对 25 或 49 个输入信号所代表的数字进行权重量化,该研究试图确定通过神经网络计算进行数字识别的可行性。在系统架构中集成忆阻器器件,可简化权重表达所需的电阻表示,从而促进基于神经网络的认知系统的实现。为了尽量减少权重量化对系统造成的信息破坏,我们在这项工作中引入了 "权重范围 "的概念。权重范围是指神经网络中权重的最大值和最小值之间的范围。我们发现,这对权重量化有直接影响,它可以将权重所代表的位数减少到一定水平以下。尽管权重水平降低了,但这有助于保持整个系统的信息完整性。此外,为了验证所提方法的有效性,量化权重被系统地应用于双层神经网络阵列。验证过程包括构建尺寸为 25 × 10 和 10 × 10 的交叉点阵列电路,然后通过设备模拟对随机生成数字的识别率变化进行细致检查。这些努力有助于推进对利用忆阻器器件和权重量化技术的基于硬件的认知计算系统的理解和实际应用。
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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来源期刊
Micromachines
Micromachines NANOSCIENCE & NANOTECHNOLOGY-INSTRUMENTS & INSTRUMENTATION
CiteScore
5.20
自引率
14.70%
发文量
1862
审稿时长
16.31 days
期刊介绍: Micromachines (ISSN 2072-666X) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to micro-scaled machines and micromachinery. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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