Parallel Matrix‐Vector Multiplication Using a Scalable Multiport Interferometer

ABSTRACT Integrated photonic matrix‐vector multiplication (MVM) is an emerging technology with broad applications in science and engineering, offering inherent advantages in high‐speed and low‐power consumption computations. Among various architectures explored, coherent multiport Mach‐Zehnder interferometer (MZI) mesh networks are widely utilized for implementing reconfigurable complex‐valued matrix computations. However, their scalability is fundamentally constrained by accumulated losses and control complexity as the mesh size grows. Here, we introduce multi‐wavelength technology to enhance computational parallelism by leveraging the broad bandwidth of MZIs, effectively adding an additional computational dimension to MZI mesh‐based MVM networks. The MZI switches are optimized for low phase error and wide bandwidth to alleviate the complexity of mesh programming and support a greater number of wavelengths. We experimentally demonstrate the mesh performing parallel MVM operations across multiple wavelengths through two applications: (1) multi‐channel analysis of clinical electrocardiogram (ECG) signals for cardiovascular risk prediction, achieving 93.8% accuracy in identifying sudden cardiac death risk; (2) parallel processing of segmented images enables edge extraction with significantly improved computational speed. This multi‐wavelength parallel MVM architecture overcomes key scalability limitations while offering a highly extensible pathway toward energy‐efficient, high‐throughput optical computing for advanced artificial neural applications.

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Publication Details

Journal
Laser & Photonics Review
Published
2025-11-12
DOI
https://doi.org/10.1002/lpor.202501876
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Parallel Matrix‐Vector Multiplication Using a Scalable Multiport Interferometer

Xiang Bai, Yao Shi, Daoxin Dai, Hongzhi Xiong et al.
Laser & Photonics Review
Neural Networks and Reservoir Computing
article

Parallel Matrix‐Vector Multiplication Using a Scalable Multiport Interferometer

Xiang Bai, Yao Shi, Daoxin Dai, Hongzhi Xiong, Yiwei Xie, S. Liu, Huan Li, Yuxin Sun, Qingying Yao, Xiyuan Ke
article en

Abstract

ABSTRACT Integrated photonic matrix‐vector multiplication (MVM) is an emerging technology with broad applications in science and engineering, offering inherent advantages in high‐speed and low‐power consumption computations. Among various architectures explored, coherent multiport Mach‐Zehnder interferometer (MZI) mesh networks are widely utilized for implementing reconfigurable complex‐valued matrix computations. However, their scalability is fundamentally constrained by accumulated losses and control complexity as the mesh size grows. Here, we introduce multi‐wavelength technology to enhance computational parallelism by leveraging the broad bandwidth of MZIs, effectively adding an additional computational dimension to MZI mesh‐based MVM networks. The MZI switches are optimized for low phase error and wide bandwidth to alleviate the complexity of mesh programming and support a greater number of wavelengths. We experimentally demonstrate the mesh performing parallel MVM operations across multiple wavelengths through two applications: (1) multi‐channel analysis of clinical electrocardiogram (ECG) signals for cardiovascular risk prediction, achieving 93.8% accuracy in identifying sudden cardiac death risk; (2) parallel processing of segmented images enables edge extraction with significantly improved computational speed. This multi‐wavelength parallel MVM architecture overcomes key scalability limitations while offering a highly extensible pathway toward energy‐efficient, high‐throughput optical computing for advanced artificial neural applications.

Laser & Photonics ReviewVol. 20(6)
China Academy of Art (CN), Jiaxing University (CN), RIKEN Center for Advanced Photonics (JP)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 82%
Neural Networks and Reservoir Computing
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