光子尖峰神经元的多光脉冲磁化反转与漏整合和发射模型

Gaku Takagi, T. Murai, Yuya Shoji
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引用次数: 0

摘要

光子加速器有望成为下一代硬件处理器,取代传统的电子加速器。在目前基于人工神经网络的光子加速器中,光子集成电路与电子集成电路结合在一起,以发挥各自的优势:光子电路用于执行线性计算,而电子电路用于执行非线性计算。然而,这种架构需要在每一层进行光电转换,无法发挥光的优势。我们提出了一种带有磁光突触和全光尖峰神经网络的新型光子尖峰神经元。这项研究通过实验证明,CoFeB 在多个光脉冲的热积累过程中发生的磁化反转,与尖峰神经元的漏电整合和发射模型的行为相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Magnetization reversal by multiple optical pulses for photonic spiking neuron with the leaky integrate and fire model
Photonic accelerators are anticipated to be the next generation of hardware processors, replacing traditional electronic accelerators. In current photonic accelerators based on artificial neural networks, photonic integrated circuits are incorporated with electronic integrated circuits to leverage their strengths: photonic circuits are used to perform linear calculations, while electronic circuits are used to perform nonlinear calculations. However, this architecture requires optoelectric conversion at each layer and is unable to leverage the superiority of light. We propose a novel photonic spiking neuron with a magneto-optical synapse and an all-optical spiking neural network. This study experimentally demonstrates that the magnetization reversal of CoFeB, which occurs during thermal accumulation owing to multiple optical pulses, is similar to the behavior of the leaky integrated and fire model of spiking neurons.
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