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arXiv 2203.01792physics.app-phphysics.optics
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光子谐振器砝码的自动化设计

Design Automation of Photonic Resonator Weights

Thomas Ferreira de Lima, Eli A. Doris, Simon Bilodeau, Weipeng Zhang, Aashu Jha, Hsuan-Tung Peng, Eric C. Blow, Chaoran Huang, Alexander N. Tait, Bhavin J. Shastri, Paul R. Prucnal

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14 pages, 9 figures
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摘要：

基于谐振器权重组的神经形态光子处理器是用于在高速模拟系统中实现现代人工智能（AI）的新兴候选技术。这些专用模拟器件利用谐振器器件的物理特性实现矢量乘法，与等效电子电路相比，具有效率、延迟和吞吐量优势。然而，伴随着这些优点，通常伴随着补偿制造变化和环境干扰的困难挑战。在本文中，我们回顾了我们的实验中的变化和干扰的来源，以及数学定义的数量，它们的模型。然后，我们将介绍如何利用谐振器的物理特性来对多波长信号进行加权和求和。最后，我们概述了必要的自动化设计和控制方法，以创建实用的，可制造的，高精度/精密度的谐振器重量银行，可以承受在现场的操作条件。这代表了在实际部署场景中释放谐振器配重块潜力的路线图。

英文摘要：

Neuromorphic photonic processors based on resonator weight banks are an emerging candidate technology for enabling modern artificial intelligence (AI) in high speed, analog systems. These purpose-built analog devices implement vector multiplications with the physics of resonator devices, offering efficiency, latency, and throughput advantages over equivalent electronic circuits. Along with these advantages, however, often comes the difficult challenges of compensation for fabrication variations and environmental disturbances. In this paper we review sources of variation and disturbances from our experiments, as well as mathematically define quantities that model them. Then, we introduce how the physics of resonators can be exploited to weight and sum multiwavelength signals. Finally, we outline automated design and control methodologies necessary to create practical, manufacturable, and high accuracy/precision resonator weight banks that can withstand operating conditions in the field. This represents a road map for unlocking the potential of resonator weight banks in practical deployment scenarios.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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