Quantization-aware Photonic Homodyne computing for Accelerated Artificial Intelligence and Scientific Simulation

TL;DR

This paper introduces a quantization-aware digital-photonic framework that enhances the accuracy and speed of photonic homodyne computing, enabling high-precision AI and scientific simulations with low latency and energy consumption.

Contribution

It presents a novel mixed-precision photonic computing approach with 6-bit precision at high speeds, overcoming analog accuracy limitations for AI and physics simulations.

Findings

Achieved 6-bit precision at 128 GS/s in photonic homodyne logic.

Demonstrated 12-bit solutions for PDEs in electromagnetic problems.

Enabled AI processing with 6 ns latency using photonic hardware.

Abstract

Modern problems in high-performance computing, ranging from training and inferencing deep learning models in computer vision and language models to simulating complex physical systems with nonlinearly-coupled equations, require exponential growth of computational resources. Photonic analog systems are emerging with solutions of intrinsic parallelism, high bandwidth, and low propagation loss. However, their application has been hindered by the low analog accuracy due to the electro-optic distortion, material nonlinearities, and signal-to-noise ratios. Here we overcome this barrier with a quantization-aware digital-photonic mixed-precision framework across chiplets for accelerated AI processing and physical simulation. Using Lithium Niobate photonics with channel equalization techniques, we demonstrate linear multiplication (9-bit amplitude-phase decoupling) in homodyne optical logics with 6-bit precision at the clock rate of 128 giga-symbol-per-second (128 GS/s), enabling AI processing with 6 ns latency. Codesign hardware-algorithms, including iterative solvers, sparse-dense quantization, and bit-sliced matrix multiplication, explore photonic amplitude and phase coherence for complex-valued, physics-inspired computation. In electromagnetic problems, our approach yields 12-bit solutions for partial differential equations (PDEs) in scattering problems that would conventionally require up to 32-bit and often even 64-bit precision. These results preserve digital-level fidelity while leveraging the high-speed low-energy photonic hardware, establishing a pathway toward general-purpose optical acceleration for generative artificial intelligence, real-time robotics, and accurate simulation for climate challenges and biological discoveries.