Intelligent photonic computing: From free-space to on-chip integration

Abstract

The increasing scalability of artificial intelligence (AI) algorithms has a high demand for efficient computing architectures and physical platforms. Intelligent photonic computing with high parallelism, high speed, low latency, and multi-dimensional data modulation properties shows its potential for dealing with current challenges of energy consumption in both computing clouds and edge devices. The implementation of photonic devices and physical computing systems for AI computing was reviewed from free-space to on-chip platforms. Firstly, the mathematics and physics of AI algorithms including feedforward neural network, recurrent neural network, and spiking neural network were introduced. Secondly, principles of devices and applications of new architectures including 3D and 2D metasurfaces which are based on diffractive deep neural network and on-chip waveguide devices including Mach–Zehnder interferometers and microring resonators were summarised. Thirdly, two emerging fields of AI computing including reconfigurability and non-linearity were surveyed, which are important factors of achieving in situ training and backpropagation towards the path of general AI computing. Finally, mainstream intelligent photonic computing platforms were compared, and the challenges were outlook.