A fully-programmable integrated photonic processor for both domain-specific and general-purpose computing

TL;DR

This paper presents a fully-programmable integrated photonic processor capable of solving complex NP-complete problems and performing general-purpose matrix computations, demonstrating high accuracy in image processing and classification tasks.

Contribution

The work introduces a versatile, fully-programmable photonic processor that can handle both domain-specific NP problems and general matrix computations, advancing optical computing capabilities.

Findings

Successfully solved NP-complete problems like subset sum and exact cover.

Achieved high-precision optical dot product for image processing.

Demonstrated 97% accuracy in MNIST classification.

Abstract

A variety of complicated computational scenarios have made unprecedented demands on the computing power and energy efficiency of electronic computing systems, including solving intractable nondeterministic polynomial-time (NP)-complete problems and dealing with large-scale artificial intelligence models. Optical computing emerges as a promising paradigm to meet these challenges, whereas current optical computing architectures have limited versatility. Their applications are usually either constrained to a specialized domain or restricted to general-purpose matrix computation. Here, we implement a fully-programmable integrated photonic processor that can be configured to tackle both specific computational problems and general-purpose matrix computation. We achieve complete end-to-end control of the photonic processor by utilizing a self-developed integrated programmable optoelectronic computing platform. For domain-specific computing, our photonic processor can efficiently solve two kinds of NP-complete problems: subset sum problem (far more than 2^N different instances) and exact cover problem. For general-purpose computation, we experimentally demonstrate high-precision optical dot product and further realize accurate image edge detection and MNIST handwritten image classification task with an accuracy of 97%. Our work enhances the versatility and capability of optical computing architecture, paving the way for its practical application in future high-performance and complex computing scenarios.