Data from: 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.