Dopant network processing units as tuneable extreme learning machines

IF 4.1 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Frontiers in Nanotechnology Pub Date : 2023-03-30 DOI:10.3389/fnano.2023.1055527
B. van de Ven, U. Alegre-Ibarra, P. J. Lemieszczuk, P. Bobbert, Hans-Christian Ruiz Euler, W. G. van der Wiel
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Abstract

Inspired by the highly efficient information processing of the brain, which is based on the chemistry and physics of biological tissue, any material system and its physical properties could in principle be exploited for computation. However, it is not always obvious how to use a material system’s computational potential to the fullest. Here, we operate a dopant network processing unit (DNPU) as a tuneable extreme learning machine (ELM) and combine the principles of artificial evolution and ELM to optimise its computational performance on a non-linear classification benchmark task. We find that, for this task, there is an optimal, hybrid operation mode (“tuneable ELM mode”) in between the traditional ELM computing regime with a fixed DNPU and linearly weighted outputs (“fixed-ELM mode”) and the regime where the outputs of the non-linear system are directly tuned to generate the desired output (“direct-output mode”). We show that the tuneable ELM mode reduces the number of parameters needed to perform a formant-based vowel recognition benchmark task. Our results emphasise the power of analog in-matter computing and underline the importance of designing specialised material systems to optimally utilise their physical properties for computation.
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作为可调极限学习机的掺杂网络处理单元
受基于生物组织化学和物理的大脑高效信息处理的启发,任何材料系统及其物理特性原则上都可以用于计算。然而,如何充分利用材料系统的计算潜力并不总是显而易见的。在这里,我们将掺杂剂网络处理单元(DNPU)作为可调谐的极限学习机(ELM)进行操作,并将人工进化和ELM的原理相结合,以优化其在非线性分类基准任务上的计算性能。我们发现,对于这项任务,在具有固定DNPU和线性加权输出的传统ELM计算机制(“固定ELM模式”)和直接调谐非线性系统的输出以产生所需输出的机制(“直接输出模式”)之间存在一种最佳的混合运行模式(“可调谐ELM模式)。我们表明,可调谐ELM模式减少了执行基于共振峰的元音识别基准任务所需的参数数量。我们的研究结果强调了模拟在物质计算中的威力,并强调了设计专门的材料系统以优化利用其物理特性进行计算的重要性。
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
Frontiers in Nanotechnology
Frontiers in Nanotechnology Engineering-Electrical and Electronic Engineering
CiteScore
7.10
自引率
0.00%
发文量
96
审稿时长
13 weeks
期刊最新文献
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