记忆电阻脉冲神经网络的设备上学习

2018 IEEE International Symposium on Circuits and Systems (ISCAS) Pub Date : 2018-05-27 DOI:10.1109/ISCAS.2018.8351813

Abdullah M. Zyarah, Nicholas Soures, D. Kudithipudi

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引用次数: 7

摘要

本文提出了一种用于器件学习的记忆电阻尖峰神经元和突触跟踪电路。这些电路的一个关键特征是使用忆阻器来模拟尖峰神经元的膜电位，而不是传统的使用电容器。电路采用IBM 65nm技术节点设计，并在小型脉冲神经网络上进行了验证。观察到3×3脉冲神经网络在100 MHz时的功耗为19.1 μW。

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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On-Device Learning in Memristor Spiking Neural Networks

In this paper, a memristor spiking neuron and synaptic trace circuits for efficient on device learning are presented. A key feature of these circuits is the use of memristors to emulate the membrane potential of spiking neurons, as opposed to the conventional use of a capacitor. The circuits are designed in IBM 65nm technology node and validated on a small-scale spiking neural network. It was observed that a 3×3 spiking neural network consumes 19.1 μW of power at 100 MHz.

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2018 IEEE International Symposium on Circuits and Systems (ISCAS)

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