The computational demands of modern AI have spurred interest in optical neural networks (ONNs) which offer the potential benefits of increased speed and lower power consumption. However, current ONNs face various challenges,most significantly a limited calculation precision (typically around 4 bits) and the requirement for high-resolution signal format converters (digital-to-analogue conversions (DACs) and analogue-to-digital conversions (ADCs)). These challenges are inherent to their analog computing nature and pose significant obstacles in practical implementation. Here, we propose a digital-analog hybrid optical computing architecture for ONNs, which utilizes digital optical inputs in the form of binary words. By introducing the logic levels and decisions based on thresholding, the calculation precision can be significantly enhanced. The DACs for input data can be removed and the resolution of the ADCs can be greatly reduced. This can increase the operating speed at a high calculation precision and facilitate the compatibility with microelectronics. To validate our approach, we have fabricated a proof-of-concept photonic chip and built up a hybrid optical processor (HOP) system for neural network applications. We have demonstrated an unprecedented 16-bit calculation precision for high-definition image processing, with a pixel error rate (PER) as low as $1.8\times10^{-3}$ at an signal-to-noise ratio (SNR) of 18.2 dB. We have also implemented a convolutional neural network for handwritten digit recognition that shows the same accuracy as the one achieved by a desktop computer. The concept of the digital-analog hybrid optical computing architecture offers a methodology that could potentially be applied to various ONN implementations and may intrigue new research into efficient and accurate domain-specific optical computing architectures for neural networks.

翻译：现代人工智能的计算需求激发了对光神经网络（ONN）的研究兴趣，这类网络具有提升速度与降低功耗的潜力。然而，当前ONN面临诸多挑战，最显著的是有限的计算精度（通常约4比特）以及对高分辨率信号格式转换器（数模转换器（DAC）与模数转换器（ADC））的需求。这些挑战源于其模拟计算的本质，并在实际应用中构成重大障碍。本文提出一种面向ONN的数模混合光计算架构，该架构采用二进制字形式的数字光输入。通过引入基于阈值判定的逻辑电平与决策机制，计算精度可得到显著提升。输入数据的DAC可被移除，ADC的分辨率也可大幅降低。这有助于在高计算精度下提升运行速度，并促进与微电子技术的兼容性。为验证所提方法，我们制备了概念验证型光子芯片，并搭建了用于神经网络应用的混合光处理器（HOP）系统。我们实现了创纪录的16比特计算精度，用于高清图像处理，在信噪比（SNR）为18.2 dB时，像素错误率（PER）低至$1.8\times10^{-3}$。我们还实现了用于手写数字识别的卷积神经网络，其准确率与台式计算机实现的准确率相同。数模混合光计算架构的概念提供了一种可应用于多种ONN实现的方法论，并可能激发面向神经网络的高效、高精度专用光计算架构的新研究。