Abstract
In emerging artificial intelligence applications, massive matrix operations require high computing speed and energy efficiency. Optical computing can realize high-speed parallel information processing with ultra-low energy consumption on photonic integrated platforms or in free space, which can well meet these domain-specific demands. In this review, we firstly introduce the principles of photonic matrix computing implemented by three mainstream schemes, and then review the research progress of optical neural networks (ONNs) based on photonic matrix computing. In addition, we discuss the advantages of optical computing architectures over electronic processors as well as current challenges of optical computing and highlight some promising prospects for the future development.
Highlights
The matrix–vector multiplication (MVM) operations enabled by photonics have a remarkably higher speed and
Fast electro-optical modulators and efficient nonlinear optical components built on the LiNbO3 -on-insulator (LNOI) platform are compatible with silicon photonic circuits [67,68,69] and provide a promising alternative approach to realize all-optical neural networks on one chip
The multiple plane light conversion (MPLC) method based on holography can achieve an ultra-large size of MVM operations due to the capability of high parallel processing in free space, and a high model complexity with millions of neurons has already been achieved with the architecture enabled by the MPLC matrix core
Summary
With the proliferation of artificial intelligence and the next-generation communication technology, the growing demand for high-performance computing has driven the development of custom hardware to accelerate this specific category of computing. The optical implementation of convolutional neural networks modulator arrays, such as electro-optic modulated direct-driven LED arrays and acoustowith fast operation speed and high energy efficiencyat is aappealing owing its outstanding optic. The optical implementation of convolutional neural networks which is a computationally intensive operation in electronics, occupies over. Of the total with fast operation speed and high energy efficiency is appealing owing to its outstanding processing time in convolutional neural networks [11], computational acceleration capability of feature extraction [10]. Convolutional processing based on for convolutional neural networks can be achieved by matching hardware and MVM. The MVM operation can be mathematically described as of the total processing time in convolutional neural networks [11], computa tional acceleration for convolutional networks be achieved wneural.
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