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.
Differentiable Neural Architecture Search for Photonic-Aware Neural Networks
Andriolli N.;Cococcioni M.
2025-01-01
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.File in questo prodotto:
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