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Scalable and compact photonic neural chip with low learning-capability-loss.
Ye Tian1,2, Yang Zhao1, Shengping Liu1
1Chongqing United Microelectronics Center (CUMEC), No. 20 Xiyuannan Road, Chongqing 100290, China.
Nanophotonics (Berlin, Germany)
|December 5, 2024
Summary
This study introduces a novel photonic matrix architecture for artificial neural networks, significantly reducing the number of Mach-Zehnder interferometers (MZIs) required. The new design offers superior performance with lower loss and power consumption for photonic neural network applications.
Area of Science:
- Photonics and Optical Engineering
- Artificial Intelligence and Machine Learning
- Integrated Optics and Photonic Circuits
Background:
- Photonic computation offers significant speed advantages for artificial neural networks (ANNs) over electronic alternatives.
- Reconfigurable photonic processors, particularly Mach-Zehnder interferometer (MZI) meshes, are key for efficient photonic matrix multiplication.
- Conventional MZI mesh architectures for N x N weight matrices require O(N^2) MZIs, limiting scalability.
Purpose of the Study:
- To propose a novel photonic matrix architecture that reduces the number of required MZIs for representing real-value matrices.
- To investigate the trade-offs between MZI reduction and learning capability loss in photonic neural networks.
- To experimentally validate the proposed architecture's performance and efficiency.
Main Methods:
- Proposed a new photonic matrix architecture utilizing the real-part of a non-universal N x N unitary MZI mesh.
- This architecture aims to represent real-value matrices with a reduced MZI requirement of O(N log2 N).
- Implemented and experimentally benchmarked a 4x4 photonic neural chip based on the proposed architecture for a convolutional neural network (CNN) handwriting recognition task.
Main Results:
- The proposed architecture significantly reduces the number of MZIs needed, from O(N^2) to O(N log2 N).
- Experimental results on a 4x4 photonic neural chip demonstrated low learning-capability loss compared to conventional O(N^2) MZI architectures.
- The new architecture exhibits all-round superiority in terms of optical loss, chip size, power consumption, and encoding error.
Conclusions:
- The proposed photonic matrix architecture offers a scalable and efficient solution for photonic matrix multiplication in ANNs.
- It achieves substantial MZI reduction with minimal impact on learning capability, making it highly promising for photonic neural network applications.
- The experimental validation confirms the practical advantages of this architecture over conventional designs.
