High-order tensor flow processing using integrated photonic circuits

Shaofu Xu, Jing Wang, Sicheng Yi and Weiwen Zou ()
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Shaofu Xu: Shanghai Jiao Tong University
Jing Wang: Shanghai Jiao Tong University
Sicheng Yi: Shanghai Jiao Tong University
Weiwen Zou: Shanghai Jiao Tong University

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions into matrix multiplications to enhance the parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we propose an integrated photonic tensor flow processor (PTFP) without digitally duplicating the input data. It outputs the convolved tensor as the input tensor ‘flows’ through the processor. The hybrid manipulation of optical wavelengths, space dimensions, and time delay steps, enables the direct representation and processing of high-order tensors in the optical domain. In the proof-of-concept experiment, an integrated processor manipulating wavelengths and delay steps is implemented for demonstrating the key functionalities of PTFP. The multi-channel images and videos are processed at the modulation rate of 20 Gbaud. A convolutional neural network for video action recognition is demonstrated on the processor, which achieves an accuracy of 97.9%.

Date: 2022
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DOI: 10.1038/s41467-022-35723-2

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