论文标题
矩阵乘法的集成光子张量处理单元：评论
Integrated Photonic Tensor Processing Unit for a Matrix Multiply: a Review
论文作者
论文摘要
人工智能和机器学习算法的爆炸与交换数据的指数增长有关，正在推动寻找新颖的应用程序特定的硬件加速器。在许多人中，由于其几乎无限的带宽能力与有限的能源消耗相关，因此光子学领域似乎是全球数据爆炸的完美聚光灯。在这篇综述中，我们将概述光子学比硬件加速器具有电子功能的主要优势，然后比较光子学整合电路（PIC）在神经网络的线性和非线性部分上实现的主要架构。到最后，我们将重点介绍下一代光子加速器的主要驱动力，以及必须克服的主要限制。
The explosion of artificial intelligence and machine-learning algorithms, connected to the exponential growth of the exchanged data, is driving a search for novel application-specific hardware accelerators. Among the many, the photonics field appears to be in the perfect spotlight for this global data explosion, thanks to its almost infinite bandwidth capacity associated with limited energy consumption. In this review, we will overview the major advantages that photonics has over electronics for hardware accelerators, followed by a comparison between the major architectures implemented on Photonics Integrated Circuits (PIC) for both the linear and nonlinear parts of Neural Networks. By the end, we will highlight the main driving forces for the next generation of photonic accelerators, as well as the main limits that must be overcome.
