论文标题
PhotoFourier：基于光子关节转换相关器的神经网络加速器
PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator
论文作者
论文摘要
在过去的几年中，要解决低延迟和高通量卷积神经网络推论的挑战。由于其低延迟性质，综合光子学具有显着加速神经网络的潜力。结合关节转换相关器（JTC）的概念，可以立即计算昂贵的卷积功能（光线飞行时间），几乎无需成本。该“自由”卷积计算提供了建议的基于JTC的CNN加速器的理论基础。 PhotoFourier解决了傅立叶域中的片上光子计算所带来的无数挑战，包括1D镜头和高成本光电转换。与最先进的光子神经网络加速器相比，所提出的光电器加速器可实现超过28倍的能量延迟产品。
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.
