Space-efficient optical computing with an integrated chip diffractive neural network

• Main Authors: Zhu, Hanhan, Zou, Jun, Zhang, Hengyi, Shi, Yuzhi, Luo, Shibo, Wang, N., Cai, H., Wan, Liangxia, Wang, Bo, Jiang, Xudong, Thompson, Jayne, Luo, Xianshu, Zhou, Xuanhe, Xiao, Limin, Huang, W., Patrick, Lento, Gu, Mile, Kwek, Leong Chuan, Liu, Ai Qun
• Other Authors: School of Physical and Mathematical Sciences
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Physics; Artificial Intelligence; Computers
• Online Access: https://hdl.handle.net/10356/160409
• Institution: Nanyang Technological University

Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing. Traditional experimental implementations need N2 units such as Mach-Zehnder interferometers (MZIs) for an input dimension N to realize typical computing operations (convolutions and matrix multiplication), resulting in limited scalability and consuming excessive power. Here, we propose the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing using two ultracompact diffractive cells (Fourier transform operation) and only N MZIs. The footprint and energy consumption scales linearly with the input data dimension, instead of the quadratic scaling in the traditional ONN framework. A ~10-fold reduction in both footprint and energy consumption, as well as equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the MNIST and Fashion-MNIST datasets. The integrated diffractive optical network (IDNN) chip demonstrates a promising avenue towards scalable and low-power-consumption optical computational chips for optical-artificial-intelligence.