Sophisticated deep learning with on-chip optical diffractive tensor processing

TL;DR

This paper introduces an on-chip optical diffractive tensor processing architecture that accelerates convolutional neural networks with high throughput, low energy, and compact design, demonstrating effective classification and denoising results.

Contribution

It presents a novel optical convolution unit (OCU) leveraging diffraction for efficient, high-speed, and energy-efficient deep learning computations on-chip.

Findings

Achieved 91.63% accuracy on Fashion-MNIST.

Demonstrated effective image denoising with PSNR up to 31.70dB.

Showed low energy consumption and high information density.

Abstract

The ever-growing deep learning technologies are making revolutionary changes for modern life. However, conventional computing architectures are designed to process sequential and digital programs, being extremely burdened with performing massive parallel and adaptive deep learning applications. Photonic integrated circuits provide an efficient approach to mitigate bandwidth limitations and power-wall brought by its electronic counterparts, showing great potential in ultrafast and energy-free high-performance computing. Here, we propose an optical computing architecture enabled by on-chip diffraction to implement convolutional acceleration, termed optical convolution unit (OCU). We demonstrate that any real-valued convolution kernels can be exploited by OCU with a prominent computational throughput boosting via the concept of structral re-parameterization. With OCU as the fundamental unit, we build an optical convolutional neural network (oCNN) to implement two popular deep learning tasks: classification and regression. For classification, Fashion-MNIST and CIFAR-4 datasets are tested with accuracy of 91.63% and 86.25%, respectively. For regression, we build an optical denoising convolutional neural network (oDnCNN) to handle Gaussian noise in gray scale images with noise level {\sigma} = 10, 15, 20, resulting clean images with average PSNR of 31.70dB, 29.39dB and 27.72dB, respectively. The proposed OCU presents remarkable performance of low energy consumption and high information density due to its fully passive nature and compact footprint, providing a highly parallel while lightweight solution for future computing architecture to handle high dimensional tensors in deep learning.