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.

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。但是，当今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其大部分是为冯·诺依曼计算方案设计的。在开发经过调整以实现具有改进的计算速度和准确性的人工神经网络的电子体系结构方面，已经做出了巨大的努力。在这里，我们提出了一种用于全光神经网络的新体系结构，该体系结构在原则上可以比用于常规推理任务的最新电子技术提高计算速度和功率效率。