Photonic integrated computing engine for concurrent optical computing

Optical networks with parallel processing capabilities advance high-speed computing and large-scale data processing by providing ultrawide computational bandwidth. In this paper, we present a photonic integrated processor that can be segmented into multiple functional blocks, enabling compact and reconfigurable matrix operations for parallel computational tasks. Fabricated on a silicon-on-insulator platform, the processor supports reconfigurable optical matrix operations of various sizes, offering flexibility and scalability. Specifically, it performs optical convolution operations with three-channel 1×1 and 2×2 real-valued convolution kernels implemented in distinct blocks. The multichannel 1×1 convolution is experimentally validated using a deep residual U-Net for precise segmentation of pneumonia lesions in lung computed tomography images. The 2×2 convolution is validated through an optical convolution layer integrated with an electrical fully connected layer for ten-class classification of handwritten digits. The processor features high scalability and robust parallel computing capability, positioning it as a promising candidate for optical neural networks. Photonic processors can accelerate large-scale data processing but often target a single fixed task. Here, the authors demonstrate a reconfigurable photonic processor that can be partitioned into functional blocks to perform three optical convolution tasks concurrently.