I/O-efficient iterative matrix inversion with photonic integrated circuits

Minjia Chen, Yizhi Wang, Chunhui Yao, Adrian Wonfor, Shuai Yang, Richard Penty and Qixiang Cheng ()
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Minjia Chen: University of Cambridge
Yizhi Wang: University of Cambridge
Chunhui Yao: University of Cambridge
Adrian Wonfor: University of Cambridge
Shuai Yang: University of Cambridge
Richard Penty: University of Cambridge
Qixiang Cheng: University of Cambridge

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Photonic integrated circuits have been extensively explored for optical processing with the aim of breaking the speed and energy efficiency bottlenecks of digital electronics. However, the input/output (IO) bottleneck remains one of the key barriers. Here we report a photonic iterative processor (PIP) for matrix-inversion-intensive applications. The direct reuse of inputted data in the optical domain unlocks the potential to break the IO bottleneck. We demonstrate notable IO advantages with a lossless PIP for real-valued matrix inversion and integral-differential equation solving, as well as a coherent PIP with optical loops integrated on-chip, enabling complex-valued computation and a net inversion time of 1.2 ns. Furthermore, we estimate at least an order of magnitude enhancement in IO efficiency of a PIP over photonic single-pass processors and the state-of-the-art electronic processors for reservoir training tasks and multiple-input and multiple-output (MIMO) precoding tasks, indicating the huge potential of PIP technology in practical applications.

Date: 2024
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DOI: 10.1038/s41467-024-50302-3

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