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 arbi..., 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., # Data from: Diffractive tensorized unit for million-TOPS general-purpose computing [https://doi.org/10.5061/dryad.7d7wm387c](https://doi.org/10.5061/dryad.7d7wm387c) Including the following datasets: **Dataset 1:** NLG (Natural Language Generation) dataset. This dataset contains raw feature data for word2vec and doc2vec training, along with a preprocessing Python file. The dataset includes multiple sub-datasets: word2vec folder contains novels (*Alice's Adventures in Wonderland*, *Harry Potter*, *The Little Prince*, *Wizard of Oz*) and doc2vec folder contains Chinese couplets, poetry, and *The Little Prince*. The preprocessing script converts text data into vector files that can be read by the training program. The dataset uses an x-in-y-out setting with fixed input and predicted words (sentences shorter than x + y are ignored), where x is set as 4, 6, 8, 10, 12, 14, and 16 and y is set as 2, 4, and 8 in the practical simulations. After 1,000 iterations, the embeddings of each selec...,