Silicon Photonics Codesign for Deep Learning
Silicon Photonics Codesign for Deep Learning
复制标题
深度学习的硅光子学协同设计
DOI：
10.1109/jproc.2020.2968184
复制
发表时间：
2020-08-01
影响因子：
20.6
通讯作者：
Bergman, Keren
中科院分区：
文献类型：
--
作者：
Cheng, Qixiang;Kwon, Jihye;Bergman, Keren
深度学习正在彻底改变我们社会的许多方面，解决从图像分类到自动驾驶汽车控制的各种决策任务。矩阵乘法是深度学习计算中必不可少的计算密集型步骤。深度神经网络的计算复杂性需要专用的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，硅光子学是一个很有前途的硬件加速平台，它可以有效地利用光学器件的固有并行性。本文详细描述了用于深度学习的相对较新且有前途的硅光子学平台的最新实现。多波长microwave硅光子架构与现场可编程门阵列（FPGA）的前处理和后处理协同设计的机会。对硅光子集成电路的详细分析表明，基于将大矩阵向量乘法分解为较小实例和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，并提高可扩展性。最后，我们提出了一个概述和设计参数的详细分析这篇文章。探讨了对前进道路的见解。
Deep learning is revolutionizing many aspects of our society, addressing a wide variety of decision-making tasks, from image classification to autonomous vehicle control. Matrix multiplication is an essential and computationally intensive step of deep-learning calculations. The computational complexity of deep neural networks requires dedicated hardware accelerators for additional processing throughput and improved energy efficiency in order to enable scaling to larger networks in the upcoming applications. Silicon photonics is a promising platform for hardware acceleration due to recent advances in CMOS-compatible manufacturing capabilities, which enable efficient exploitation of the inherent parallelism of optics. This article provides a detailed description of recent implementations in the relatively new and promising platform of silicon photonics for deep learning. Opportunities for multiwavelength microring silicon photonic architectures codesigned with field-programmable gate array (FPGA) for pre- and postprocessing are presented. The detailed analysis of a silicon photonic integrated circuit shows that a codesigned implementation based on the decomposition of large matrix-vector multiplication into smaller instances and the use of nonnegative weights could significantly simplify the photonic implementation of the matrix multiplier and allow increased scalability. We conclude this article by presenting an overview and a detailed analysis of design parameters. Insights for ways forward are explored.