通过电流脉冲控制实现的H x wo3神经形态装置的突触特性增强,用于人工神经网络

D. Nishioka, T. Tsuchiya, T. Higuchi, K. Terabe
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引用次数: 1

摘要

人工突触能够模仿生物突触的基本功能,这对于构建高效的神经形态系统至关重要。我们开发了一种基于羟基wo3的人工突触,通过全固态氧化还原晶体管机制复制了这种突触功能。本课题的synaptic- h x WO3晶体管通过电流脉冲控制工作,具有良好的线性度、低更新变化和电导调制特性。我们研究了该设备在各种工况下的性能,以及设备特性对人工神经网络计算的影响。虽然被试synaptic- h x WO3晶体管在电压脉冲控制下的手写数字识别任务的识别精度不足66%,但在电流脉冲控制下,它的识别精度达到了88%,接近理想突触器件的93%的精度。这一结果表明,电流脉冲控制可以显著改善任何氧化还原晶体管型人工突触的性能,从而为进一步探索和进化高级神经形态系统铺平了道路,并有可能彻底改变人工智能领域。它进一步标志着向实现高性能、低功耗计算设备迈出了重要的一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced synaptic characteristics of H x WO3-based neuromorphic devices, achieved by current pulse control, for artificial neural networks
Artificial synapses capable of mimicking the fundamental functionalities of biological synapses are critical to the building of efficient neuromorphic systems. We have developed a H x WO3-based artificial synapse that replicates such synaptic functionalities via an all-solid-state redox transistor mechanism. The subject synaptic-H x WO3 transistor, which operates by current pulse control, exhibits excellent synaptic properties including good linearity, low update variation and conductance modulation characteristics. We investigated the performance of the device under various operating conditions, and the impact of the characteristics of the device on artificial neural network computing. Although the subject synaptic-H x WO3 transistor showed an insufficient recognition accuracy of 66% for a handwritten digit recognition task with voltage pulse control, it achieved an excellent accuracy of 88% with current pulse control, which is approaching the 93% accuracy of an ideal synaptic device. This result suggests that the performance of any redox-transistor-type artificial synapse can be dramatically improved by current pulse control, which in turn paves the way for further exploration and the evolution of advanced neuromorphic systems, with the potential to revolutionize the artificial intelligence domain. It further marks a significant stride towards the realization of high-performance, low-power consumption computing devices.
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CiteScore
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