Silicon Photonics Codesign for Deep Learning

doi:10.1109/JPROC.2020.2968184

2020-08-01

DOI：10.1109/JPROC.2020.2968184

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摘要

En 中文

深度学习正在彻底改变我们社会的许多方面，解决各种各样的决策任务，从图像分类到自动驾驶汽车控制。矩阵乘法是深度学习计算中必不可少且计算密集的步骤。深度神经网络的计算复杂性需要专用硬件加速器来实现额外的处理吞吐量和提高的能效，以便在即将到来的应用中扩展到更大的网络。由于CMOS兼容制造能力的最新进展，硅光子学是用于硬件加速的有前途的平台，其使得能够有效地利用光学器件的固有并行性。本文提供了用于深度学习的相对较新且有前途的硅光子学平台中最新实现的详细描述。提出了与现场可编程门阵列 (FPGA) 共同设计用于预处理和后处理的多波长微环硅光子架构的机会。对硅光子集成电路的详细分析表明，基于将大型矩阵向量乘法分解为较小实例并使用非负权重的协同设计实现可以显着简化矩阵乘法器的光子实现，并允许增加可扩展性。我们通过对设计参数的概述和详细分析来结束本文。探索了前进道路的见解。

Keyword:

Photonics

Silicon

Deep learning

Optical resonators

Task analysis

Integrated optics

Convolutional codes

Deep learning

microring resonator (MRR)

neural network

photonic integrated circuit (PIC)

silicon photonics

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期刊

Proceedings of the IEEE

IF:

25.9

论文数:

9.9K

被引数:

4.5W

机构

Columbia University

学者数:

7.1W

论文数: 6.4W

被引数: 263

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