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 come 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.

中文翻译：

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光子谐振器重量的设计自动化

基于谐振器重量组的神经形态光子处理器是一种新兴的候选技术，可在高速模拟系统中实现现代人工智能 (AI)。这些专用模拟设备利用谐振器设备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，伴随着这些优势，通常会遇到对制造变化和环境干扰进行补偿的困难挑战。在本文中，我们回顾了实验中的变化和干扰源，并在数学上定义了模拟它们的量。然后，我们介绍了如何利用谐振器的物理特性对多波长信号进行加权和求和。最后，我们概述了创建可承受现场操作条件的实用、可制造和高精度/精密谐振器配重组所必需的自动化设计和控制方法。这代表了在实际部署场景中释放谐振器重量组潜力的路线图。