Unlocking High-Speed and Energy-Efficiency: Integrated Convolution Processing on Thin-Film Lithium Niobate

Abstract

Optical neural networks (ONNs) have emerged as high-performance neural network accelerators, owing to its broad bandwidth and low power consumption. However, most current ONN architectures still struggle to fully leverage their advantages in processing speed and energy efficiency. Here, we demonstrate a large-scale, ultra-high-speed, and low-power ONN distributed parallel computing architecture, implemented on a thin-film lithium niobate platform. It can encode image information at a modulation rate of 128 Gbaud and perform 16 parallel 2 × 2 convolution kernel operations, achieving 8.190 trillion multiply-accumulate operations per second (TMACs/s) with a power efficiency of 4.55 tera operations per second per watt (Tops/W). This work conducts proof-of-concept experiments for image edge detection and three different ten-class dataset recognitions, showing performance comparable to digital computers. Thanks to its excellent scalability, high speed, and low power consumption, the integrated distributed parallel optical computing architecture shows great potential to perform much more sophisticated tasks for demanding applications, such as autonomous driving and video action recognition.