Integrated photonic FFT for photonic tensor operations towards efficient and high-speed neural networks

Nanophotonics

article

Moustafa Ahmed¹  Yas Al-Hadeethi¹  Ahmed Bakry¹  Hamed Dalir²  Volker J. Sorger³ 

[1] Department of Physics, Faculty of Science, King Abdulaziz University;Inc. 8500 Shoal Creek Blvd.;Department of Electrical and Computer Engineering, George Washington University

关键词: integrated photonic;    metasurface;    neural networks;    optical convolutions;   

DOI  :  10.1515/nanoph-2020-0055

学科分类：社会科学、人文和艺术（综合）

来源: De Gruyter

【 摘 要 】

The technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific architectures such as in graphics processing units (GPU). However, electronics systems are limited with respect to power dissipation and delay, due to wire-charging challenges related to interconnect capacitance. Here we present a silicon photonics-based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently by executing the convolution as a multiplication in the Fourier-domain. The algorithmic executing time is determined by the time-of-flight of the signal through this photonic reconfigurable passive FFT ‘filter’ circuit and is on the order of 10’s of picosecond short. A sensitivity analysis shows that this optical processor must be thermally phase stabilized corresponding to a few degrees. Furthermore, we find that for a small sample number, the obtainable number of convolutions per {time, power, and chip area) outperforms GPUs by about two orders of magnitude. Lastly, we show that, conceptually, the optical FFT and convolution-processing performance is indeed directly linked to optoelectronic device-level, and improvements in plasmonics, metamaterials or nanophotonics are fueling next generation densely interconnected intelligent photonic circuits with relevance for edge-computing 5G networks by processing tensor operations optically.

【 授权许可】

CC BY