用于非线性全光计算的完整光子集成神经元。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Tao Yan, Yanchen Guo, Tiankuang Zhou, Guocheng Shao, Shanglong Li, Ruqi Huang, Qionghai Dai, Lu Fang
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引用次数: 0

摘要

光子神经网络在实现超快速人工智能推理和解决对计算速度和能源效率不断增长的需求方面具有巨大的潜力,因此该领域经历了大幅增长。然而,实现非线性完全全光神经元仍然具有挑战性,这限制了光子神经网络的性能。本文报道了一种具有时空特征学习能力和可重构结构的完整光子集成神经元(PIN),用于非线性全光计算。通过交错光子的时空维度和利用克尔效应,PIN在氮化硅光子芯片上单片执行高阶时间卷积和全光非线性激活,实现加权互连和非线性的神经元完备性。我们开发了PIN芯片系统,并证明了其在高精度图像分类和人体运动生成方面的卓越性能。PIN实现了超快的时空处理,延迟低至240 ps,为将机器智能推进到亚纳秒级铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A complete photonic integrated neuron for nonlinear all-optical computing.

The field of photonic neural networks has experienced substantial growth, driven by its potential to enable ultrafast artificial intelligence inference and address the escalating demand for computing speed and energy efficiency. However, realizing nonlinearity-complete all-optical neurons is still challenging, constraining the performance of photonic neural networks. Here we report a complete photonic integrated neuron (PIN) with spatiotemporal feature learning capabilities and reconfigurable structures for nonlinear all-optical computing. By interleaving the spatiotemporal dimension of photons and leveraging the Kerr effect, PIN performs high-order temporal convolution and all-optical nonlinear activation monolithically on a silicon-nitride photonic chip, achieving neuron completeness of weighted interconnects and nonlinearities. We develop the PIN chip system and demonstrate its remarkable performance in high-accuracy image classification and human motion generation. PIN enables ultrafast spatialtemporal processing with a latency as low as 240 ps, paving the way for advancing machine intelligence into the subnanosecond regime.

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