2023/08/16 by Shiji Zhang, Haojun Zhou, Zhang, Shiji +13 · 1 citation
Computer Science · Engineering · #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Neural Networks and Reservoir Computing #Optical Network Technologies #Optics (physics.optics) #Photonic and Optical Devices
paper · pdf · doi:10.48550/arxiv.2308.08236
openalex publication_date 2023/08/16 · openalex created_date 2023/08/18 · openalex updated_date 2026/07/28
Optical neural networks (ONNs) have gained significant attention due to their potential for high-speed and energy-efficient computation in artificial intelligence. The implementation of optical convolutions plays a vital role in ONNs, as they are fundamental operations within neural network architectures. However, state-of-the-art convolution architectures often suffer from redundant inputs, leading to substantial resource waste. Here, we propose an integrated optical convolution architecture that leverages the inherent routing principles of arrayed waveguide grating (AWG) to execute the sliding of convolution kernel and summation of results. M*N multiply-accumulate (MAC) operations are facilitated by M+N units within a single clock cycle, thus eliminating the redundancy. In the experiment, we achieved 5-bit precision and 91.9% accuracy in the handwritten digit recognition task confirming the reliability of our approach. Its redundancy-free architecture, low power consumption, high compute density (8.53 teraOP mm-2 s-1) and scalability make it a valuable contribution to the field of optical neural networks, thereby paving the way for future advancements in high-performance computing and artificial intelligence applications.