Metasurface-empowered freely-arrangeable multi-task diffractive neural networks with weighted training

TL;DR

This paper introduces a reconfigurable diffractive neural network architecture that uses layer rearrangement and weighted training to perform multiple tasks efficiently at light speed, demonstrated through simulations and experiments.

Contribution

It presents a novel arrangeable diffractive neural network with low-cost reconfiguration and multi-task capabilities using weighted training.

Findings

Achieves dynamic reordering of layers for multi-task processing.

Demonstrates high accuracy in recognizing digits and fashion items.

Operates effectively at terahertz frequencies.

Abstract

Recent advancements in optical computing have garnered considerable research interests owing to its ener-gy-efficient operation and ultralow latency characteristics. As an emerging framework in this domain, dif-fractive deep neural networks (D2NNs) integrate deep learning algorithms with optical diffraction principles to perform computational tasks at light speed without requiring additional energy consumption. Neverthe-less, conventional D2NN architectures face functional limitations and are typically constrained to single-task operations or necessitating additional costs and structures for functional reconfiguration. Here, an arrangea-ble diffractive neural network (A-DNN) that achieves low-cost reconfiguration and high operational versa-tility by means of diffractive layer rearrangement is presented. Our architecture enables dynamic reordering of pre-trained diffractive layers to accommodate diverse computational tasks. Additionally, we implement a weighted multi-task loss function that allows precise adjustment of task-specific performances. The efficacy of the system is demonstrated by both numerical simulations and experimental validations of recognizing handwritten digits and fashions at terahertz frequencies. Our proposed architecture can greatly expand the flexibility of D2NNs at a low cost, providing a new approach for realizing high-speed, energy-efficient ver-satile artificial intelligence systems.