2025-09-08 アメリカ合衆国・フロリダ大学 (UF)
＜関連情報＞
- https://news.ufl.edu/2025/09/optical-ai-chip/
- https://www.spiedigitallibrary.org/journals/advanced-photonics/volume-7/issue-05/056007/Near-energy-free-photonic-Fourier-transformation-for-convolution-operation-acceleration/10.1117/1.AP.7.5.056007.full
畳み込み演算の高速化のためのエネルギーフリーに近い光子フーリエ変換 Near-energy-free photonic Fourier transformation for convolution operation acceleration
Hangbo Yang, Nicola Peserico, Shurui Li, Xiaoxuan Ma, Russell L. T. Schwartz, Mostafa Hosseini, Aydin Babakhani, Chee Wei Wong, Puneet Gupta, Volker J. Sorger
Advanced Photonics Published:8 September 2025
DOI:https://doi.org/10.1117/1.AP.7.5.056007
Abstract
Convolutional operations are computationally intensive in artificial intelligence (AI) services, and their overhead in electronic hardware limits machine learning scaling. Here, we introduce a photonic joint transform correlator (pJTC) using a near-energy-free on-chip Fourier transformation to accelerate convolution operations. The pJTC reduces computational complexity for both convolution and cross-correlation from O(N4) to O(N2), where N2 is the input data size. Demonstrating functional Fourier transforms and convolution, this pJTC achieves 98.0% accuracy on an exemplary Modified National Institute of Standards and Technology inference task. Furthermore, a wavelength-multiplexed pJTC architecture shows potential for high throughput and energy efficiency, reaching 305TOPS/W and 40.2TOPS/mm2, based on currently available foundry processes. An efficient, compact, and low-latency convolution accelerator promises to advance next-generation AI capabilities across edge demands, high-performance computing, and cloud services.
