Global-Power-Split-Tree Architecture for Large-Scale Coherent Optical Matrix Multiplication

作者：Sicheng Yi, Yuting Chen, Shaoyang Zhang, Hangyu Shi, Binshuo Liu, Shaofu Xu, and Weiwen Zou*
摘要：Photonics holds the physical potential for achieving both high-speed and low-consumption matrix multiplication. Nonetheless, due to the insertion loss of optical phase shifters and the loss incurred from power splitting, traditional methods are challenging to achieve high throughput, precision, and energy efficiency within a single framework. In this letter, we propose a global-power-split-tree (GPST) architecture for large-scale coherent optical matrix multiplication. The insertion loss of phase shifters decreases from O(N ) to O(logN ) when utilizing phase shifters in the form of splittree configurations. The architecture enhances energy utilization from O(1/N^2) to O(1/N ) through global power allocation. Theoretical analysis and simulation show that, compared with conventional coherent optical matrix multiplication architectures, GPST can achieve near 10× higher energy efficiency. By employing five different neural networks to illustrate the overall power consumption of GPST, we find that compared to conventional architectures, power consumption decreases by an order of magnitude, especially when the integration scale is large, compared to conventional architectures.
出版源：PHYSICAL REVIEW APPLIED
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