PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator

Autor:	Li, Shurui, Yang, Hangbo, Wong, Chee Wei, Sorger, Volker J., Gupta, Puneet
Rok vydání:	2022
Předmět:	Computer Science - Hardware Architecture Computer Science - Emerging Technologies Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference. Integrated photonics has the potential to dramatically accelerate neural networks because of its low-latency nature. Combined with the concept of Joint Transform Correlator (JTC), the computationally expensive convolution functions can be computed instantaneously (time of flight of light) with almost no cost. This 'free' convolution computation provides the theoretical basis of the proposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses a myriad of challenges posed by on-chip photonic computing in the Fourier domain including 1D lenses and high-cost optoelectronic conversions. The proposed PhotoFourier accelerator achieves more than 28X better energy-delay product compared to state-of-art photonic neural network accelerators. Comment: 12 pages, 13 figures, accepted in HPCA 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.05276 Zobrazit plný text záznamu View this record from Arxiv