Optical processing of information holds great promise for addressing many challenges facing the field of computing. However, integrated photonic processors are typically limited by the physical size of the processing units and the energy consumption of high-speed analog-to-digital conversion. In this paper, we demonstrate an integrated, coherent approach to processing temporally multiplexed optical signals using a modular dot-product unit cell to address these challenges. We use these unit cells to demonstrate multiply-accumulate operations on real- and complex-valued inputs using coherent detection and temporal integration. We then extend this to computing the covariance between stochastic bit streams, which can be used to estimate correlation between data streams in the optical domain. Finally, we demonstrate a path to scaling up our platform to enable general matrix-matrix operations. Our approach has the potential to enable highly efficient and scalable optical computing on-chip for a broad variety of AI applications.

中文翻译：

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实现可扩展矩阵运算的集成相干光子平台

信息的光学处理对于解决计算领域面临的许多挑战具有巨大的前景。然而，集成光子处理器通常受到处理单元的物理尺寸和高速模数转换的能耗的限制。在本文中，我们展示了一种集成的、相干的方法，使用模块化点积单元来处理时间复用光信号，以应对这些挑战。我们使用这些单位单元通过相干检测和时间积分来演示对实值和复值输入的乘法累加运算。然后，我们将其扩展到计算随机比特流之间的协方差，这可用于估计光域中数据流之间的相关性。最后，我们展示了扩展我们的平台以实现通用矩阵-矩阵运算的途径。我们的方法有潜力为各种人工智能应用实现高效且可扩展的片上光学计算。