Differentiable Neural Architecture Search for Photonic-Aware Neural Networks

We propose a differentiable Neural Architecture Search framework for photonic-aware neural networks integrating photonic constraints and quantization-aware training. The resulting architectures outperform handcrafted models achieving 79% accuracy at 6-bit quantization, closely matching full-precision baselines.

Authors

Nicola Andriolli (ORCID: https://orcid.org/0000-0001-8322-6854)
Emilio Paolini (ORCID: https://orcid.org/0000-0001-7486-9268)
Marco Cococcioni (ORCID: https://orcid.org/0000-0002-7020-1524)
Gianluca Paolini (ORCID: https://orcid.org/0000-0002-8266-362X)

Institutions

University of Pisa (IT)
Scuola Superiore Sant'Anna (IT)

Publication Details

Published: 2025-06-29
DOI: https://doi.org/10.23919/oecc/psc62146.2025.11109505
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Differentiable Neural Architecture Search for Photonic-Aware Neural Networks

Nicola Andriolli, Emilio Paolini, Marco Cococcioni, Gianluca Paolini

Jun 29, 2025

Neural Networks and Reservoir Computing

article

Differentiable Neural Architecture Search for Photonic-Aware Neural Networks

Nicola Andriolli, Emilio Paolini, Marco Cococcioni, Gianluca Paolini

June 29, 2025article en

Abstract

We propose a differentiable Neural Architecture Search framework for photonic-aware neural networks integrating photonic constraints and quantization-aware training. The resulting architectures outperform handcrafted models achieving 79% accuracy at 6-bit quantization, closely matching full-precision baselines.

University of Pisa (IT), Scuola Superiore Sant'Anna (IT)

Openalex Percentile: Top 89%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

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Differentiable Neural Architecture Search for Photonic-Aware Neural Networks — Nicola Andriolli, Emilio Paolini, et al. · (2025) | TGRS Research Map | TGRS