Diffractive tensorized unit for million-TOPS general-purpose computing

Photonic computing has emerged as a promising next-generation technology for processors, with diffraction-based architectures showing particular potential for large-scale parallel processing. Unfortunately, the lack of on-chip reconfigurability poses significant obstacles to realizing general-purpose computing, restricting the adaptability of these architectures to diverse advanced applications. We propose a diffractive tensorized unit (DTU), which is a fully reconfigurable photonic processor supporting million-TOPS general-purpose computing. The DTU leverages a tensor factorization approach to perform complex matrix multiplication through clustered diffractive tensor cores (DTCs), while each DTC employs a near-core modulation mechanism to activate dynamic temporal diffractive connections. Experiments confirm that the DTU overcomes the long-standing generality and scalability constraints of diffractive computing, realizing general computing with a 10-6 mean absolute error (MAE) for arbitrary 1,024-size matrix multiplications. Compared with state-of-the-art electronic-based solutions, the DTU not only achieves competitive accuracy on various challenging tasks, such as natural language generation and cross-modal recognition, but also delivers a remarkable 1,000X improvement in throughput over conventional electronic processors. The proposed DTU represents a leap forward in general-purpose photonic computing, paving the way for further advancements in large-scale artificial intelligence.

Methods
The original data was preprocessed to align with the architectural specifications of our DTU optical computing hardware. This involved image resizing, cropping, and a reorganization of the training-testing splits to optimize computational efficiency.