近日，英国牛津大学的Harish Bhaskaran及其研究团队取得一项新进展。经过不懈努力，他们发现部分相干能够增强并行光子计算。相关研究成果已于2024年7月31日在国际权威学术期刊《自然》上发表。

该研究团队在两个光子计算应用平台上展示了他们的系统：一个是使用相变材料光子存储器的光子张量核，它能够提供并行卷积运算，并以92.2%的准确率（理论准确率为92.7%）对10名帕金森病患者的步态进行了分类；另一个则是嵌入了电吸收调制器（EAMs）的硅光子张量核，它能够实现每秒0.108 tera的运算（TOPS）卷积处理，并在对修正的国家标准与技术研究所（MNIST）手写数字数据集进行分类时达到了92.4%的准确率（理论准确率为95.0%）。

据悉，光学相干控制技术的进步已经为众多尖端应用开辟了道路，这些应用包括长途通信、光探测和测距（即激光雷达）以及光学相干断层扫描。传统观点认为，增加相干光源的使用可以提升系统性能和器件功能。近期，一种新型的光子卷积处理系统被引入，该系统利用部分相干光来提高计算并行性，同时不会显著牺牲计算精度，有望实现更大规模的光子张量核。值得注意的是，相干度的适度降低反而优化了光子卷积处理系统中带宽的利用效率。这一突破挑战了传统观念，即相干性在集成光子加速器中是必不可少的，甚至是有利的，从而使高通量光子计算能够使用不那么严格的反馈控制和热管理要求的光源。

附：英文原文

Title: Partial coherence enhances parallelized photonic computing

Author: Dong, Bowei, Brckerhoff-Plckelmann, Frank, Meyer, Lennart, Dijkstra, Jelle, Bente, Ivonne, Wendland, Daniel, Varri, Akhil, Aggarwal, Samarth, Farmakidis, Nikolaos, Wang, Mengyun, Yang, Guoce, Lee, June Sang, He, Yuhan, Gooskens, Emmanuel, Kwong, Dim-Lee, Bienstman, Peter, Pernice, Wolfram H. P., Bhaskaran, Harish

Issue&Volume: 2024-07-31

Abstract: Advancements in optical coherence control have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson’s disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

DOI: 10.1038/s41586-024-07590-y

Source: https://www.nature.com/articles/s41586-024-07590-y