Complete photonic tensor convolution driven by single dataflow

Current photonic convolutional processors transform tensor convolutions into multi-channel general matrix multiplication (GeMM), leading to data replication and hardware complexity. In this study, we propose and experimentally demonstrate a photonic tensor processing unit (PTPU) with single dataflow...

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Bibliographic Details
Published in2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM) pp. 1 - 3
Main Authors Tang, Kaifei, Wang, Jiantao, Ji, Xiang, Liu, Jiahui, Xin, Yu, Cao, Haijiang, Zeng, Zhaobang, Xiao, Rulei, Jiang, Wei
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.11.2023
Subjects
Feature extraction
general matrix multiplication
Modulation
Neural networks
Parallel processing
photonic tensor processing convolution
Program processors
single dataflow
Tensors
Transforms
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DOI10.1109/ACP/POEM59049.2023.10369087

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Summary:Current photonic convolutional processors transform tensor convolutions into multi-channel general matrix multiplication (GeMM), leading to data replication and hardware complexity. In this study, we propose and experimentally demonstrate a photonic tensor processing unit (PTPU) with single dataflow, which offers a more concise approach to multi-channel standard tensor convolution processing. In experiment, we extracted features from a 3-channel (RGB) image in horizontal and vertical directions using an integrated multi-wavelength source. We then built a 3\mathrm{D} -convolutional neural network to predict the presence of COVID-19 based on computer tomography (CT) scan data consisting of 64-channel tensors.
DOI:10.1109/ACP/POEM59049.2023.10369087