Photonic convolutional neural network with pre-trained in-situ training

TL;DR

This paper presents a fully photonic CNN capable of MNIST classification with high accuracy, leveraging in-situ training and silicon photonics to achieve superior energy efficiency and robustness.

Contribution

It introduces a novel photonic CNN architecture with in-situ training using a digital twin and SPSA, eliminating optical-electrical conversions for improved efficiency.

Findings

Achieved 94% test accuracy on MNIST

Demonstrated 100-242x energy efficiency over GPUs

Maintained robustness with only 0.43% accuracy loss under thermal crosstalk

Abstract

Photonic computing is a computing paradigm which have great potential to overcome the energy bottlenecks of electronic von Neumann architecture. Throughput and power consumption are fundamental limitations of Complementary-metal-oxide-semiconductor (CMOS) chips, therefore convolutional neural network (CNN) is revolutionising machine learning, computer vision and other image based applications. In this work, we propose and validate a fully photonic convolutional neural network (PCNN) that performs MNIST image classification entirely in the optical domain, achieving 94 percent test accuracy. Unlike existing architectures that rely on frequent in-between conversions from optical to electrical and back to optical (O/E/O), our system maintains coherent processing utilizing Mach-Zehnder interferometer (MZI) meshes, wavelength-division multiplexed (WDM) pooling, and microring resonator-based nonlinearities. The max pooling unit is fully implemented on silicon photonics, which does not require opto-electrical or electrical conversions. To overcome the challenges of training physical phase shifter parameters, we introduce a hybrid training methodology deploying a mathematically exact differentiable digital twin for ex-situ backpropagation, followed by in-situ fine-tuning via Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. Our evaluation demonstrates significant robustness to thermal crosstalk (only 0.43 percent accuracy degradation at severe coupling) and achieves 100 to 242 times better energy efficiency than state-of-the-art electronic GPUs for single-image inference.