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A small microring array that performs large complex-valued matrix-vector multiplication

delete2022-04-28
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OA
AI
J
Junwei Cheng
Y
Yuhe Zhao
W
Wenkai Zhang
H
Hailong Zhou
D
Dongmei Huang
Q
Qing Zhu
Y
Yuhao Guo
X
Xu, Bo
J
Jianji Dong *
张新亮 cover
张新亮 (Xinliang Zhang)
DOI:10.1007/s12200-022-00009-4delete
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Abstract

Abstract

En 中文
As an important computing operation, photonic matrix-vector multiplication is widely used in photonic neutral networks and signal processing. However, conventional incoherent matrix-vector multiplication focuses on real-valued operations, which cannot work well in complex-valued neural networks and discrete Fourier transform. In this paper, we propose a systematic solution to extend the matrix computation of microring arrays from the real-valued field to the complex-valued field, and from small-scale (i.e., 4 x 4) to large-scale matrix computation (i.e., 16 x 16). Combining matrix decomposition and matrix partition, our photonic complex matrix-vector multiplier chip can support arbitrary large-scale and complex-valued matrix computation. We further demonstrate Walsh-Hardmard transform, discrete cosine transform, discrete Fourier transform, and image convolutional processing. Our scheme provides a path towards breaking the limits of complex-valued computing accelerator in conventional incoherent optical architecture. More importantly, our results reveal that an integrated photonic platform is of huge potential for large-scale, complex-valued, artificial intelligence computing and signal processing.
Keywords:
Photonic matrix-vector multiplication
Complex-valued computing
Microring array
Signal
image processing

Journal

Frontiers of Optoelectronics cover
Frontiers of Optoelectronics
IF:
5.2
Papers:
250
Citations:
1.3K

Organization

H
huawei technologies
Scholars:
3.3K
Papers: 2.9K
Citations: 1
H
hong kong polytechnic university
Scholars:
3.0W
Papers: 4.1W
Citations: 921
Cited Papers

Cited Papers

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Photonic tensor cores for machine learning
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The discrete cosine transform
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errStrang, G
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