Photonic neural networks offer energy-efficiency but suffer from noise-induced low precision. We propose AQ-PANN, which learns a quantization step size to mitigate noise. Experiments on SVHN show strong performance across bitwidths under different noise levels.
Mitigating Noise Effects in Photonic Neural Networks Using Adaptive Quantization
Paolini, E.;De Marinis, L.;Kincaid, P. S.;Valcarenghi, L.;Contestabile, G.;Andriolli, N.
2025-01-01
Abstract
Photonic neural networks offer energy-efficiency but suffer from noise-induced low precision. We propose AQ-PANN, which learns a quantization step size to mitigate noise. Experiments on SVHN show strong performance across bitwidths under different noise levels.File in questo prodotto:
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