卷积(计算机科学)
光子学
计算机科学
延迟(音频)
卷积神经网络
计算
人工神经网络
推论
傅里叶变换
吞吐量
计算科学
电子工程
人工智能
算法
光学
电信
物理
工程类
量子力学
无线
作者
Shurui Li,Hangbo Yang,Chee Wei Wong,Volker J. Sorger,Puneet Gupta
标识
DOI:10.1109/hpca56546.2023.10070931
摘要
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 28× better energy-delay product compared to state-of-art photonic neural network accelerators.
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