Work

In situ fine-tuning of in silico trained Optical Neural Networks

Year: 2026

Type: article

Abstract: Optical Neural Networks (ONNs) offer ultrafast computation, high bandwidth, and low energy use by exploiting photonic hardware. Training, however, remains difficult since parameters learned on simplified in silico models often degrade when transferred to physical devices with noise and fabrication imperfections. We study how noise misspecification during in silico training impacts performance and propose Gradient-Informed Fine-Tuning (GIFT). This is a lightweight in situ algorithm that adapts pretrained parameters. GIFT requires no full retraining or elaborate setups and comes with mathematical guarantees on when it improves performance. Simulations on a five-layer ONN for MNIST classification show that GIFT yields up to 28% relative accuracy improvement under noise misspecification. Thus, GIFT offers a practical means to close the gap between digital models and physical ONNs.

Source: ACM SIGMETRICS Performance Evaluation Review

DOI: 10.1145/3797823.3797848

Authors: Gianluca Kosmella, Ripalta Stabile, Jaron Sanders

Institution: Eindhoven University of Technology

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Awards: NETWORKS Doctoral Program, G565383568