Deep learning with coherent nanophotonic circuits
Deep learning with coherent nanophotonic circuits
复制标题
DOI：
10.1038/nphoton.2017.93
复制
发表时间：
2017-07-01
期刊：
影响因子：
35
通讯作者：
Soljacic, Marin
中科院分区：
文献类型：
--
作者：
Shen, Yichen;Harris, Nicholas C.;Soljacic, Marin
人工神经网络是受大脑信号处理启发的计算网络模型。这些模型极大地提高了许多机器学习任务的性能，包括语音和图像识别。然而，今天的计算硬件在实现神经网络方面效率低下，这在很大程度上是因为它的大部分是为冯·诺依曼计算方案设计的。已经做出了大量努力来开发被调谐以实现表现出改进的计算速度和准确性的人工神经网络的电子架构。在这里，我们提出了一种全光学神经网络的新架构，原则上，与用于传统推理任务的最先进电子设备相比，该架构可以提高计算速度和功率效率。我们实验证明的概念，使用可编程纳米光子处理器具有级联阵列的56个可编程马赫-曾德尔干涉仪在硅光子集成电路的重要组成部分，并显示其实用程序的元音识别。
Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.