Programmable electro-optic frequency comb empowers integrated parallel convolution processing

TL;DR

This paper introduces a programmable electro-optic frequency comb that significantly enhances integrated photonic convolution processors, enabling high-speed, scalable, and reconfigurable optical neural network operations for AI applications.

Contribution

It presents the first integrated photonic processing unit with multi-wavelength generation and weight mapping via a single programmable EO comb, achieving unprecedented speed and efficiency.

Findings

Processing speed of 1.62 TOPS achieved

Weight reconstruction speed exceeds 38 GHz

Demonstrated image edge detection and object classification

Abstract

Integrated photonic convolution processors make optical neural networks (ONNs) a transformative solution for artificial intelligence applications such as machine vision. To enhance the parallelism, throughput, and energy efficiency of ONNs, wavelength multiplexing is widely applied. However, it often encounters the challenges of low compactness, limited scalability, and high weight reconstruction latency. Here, we proposed and demonstrated an integrated photonic processing unit with a parallel convolution computing speed of 1.62 trillion operations per second (TOPS) and a weight reconstruction speed exceeding 38 GHz. This processing unit simultaneously achieves, for the first time, multi-wavelength generation and weight mapping via a single programmable electro-optic (EO) frequency comb, featuring unprecedented compactness, device-footprint independent scalability, and near-unity optical power conversion efficiency (conversion efficiency from input optical power to output weighted comb lines). To demonstrate the reconfigurability and functionality of this processing unit, we implemented image edge detection and object classification based on EO combs obtained using the particle swarm algorithm and an EO comb neural network training framework, respectively. Our programmable EO comb-based processing framework establishes a new paradigm towards the development of low-latency monolithic photonic processors, promising real-time in-sensor learning for autonomous vehicles, intelligent robotics, and drones.