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New LCAP framework bridges simulation-to-hardware gap for photonic neural networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Latent Chip Adaptation from Probes (LCAP) to address the simulation-to-hardware gap in photonic neural networks (PNNs). This method decomposes hardware adaptation into a transferable population correction and a personalized latent correction. By learning from historical chip data and using a small number of output probes, LCAP can significantly improve the accuracy of unseen chips, raising worst-device accuracy from 89.18% to 90.54% in simulations. AI

IMPACT Improves accuracy and reduces calibration costs for photonic neural networks, potentially enabling more efficient hardware deployment.

RANK_REASON The cluster contains a research paper detailing a new framework for photonic neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New LCAP framework bridges simulation-to-hardware gap for photonic neural networks

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The cluster contains a research paper detailing a new framework for photonic neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tianyu Gao, Guantian Zheng ·

    LCAP: Population-Informed Latent Chip Adaptation from Few Output Probes for Photonic Neural Networks

    arXiv:2609.16823v1 Announce Type: new Abstract: Photonic neural networks (PNNs) offer efficient analog inference, but parameters optimized under ideal device models can degrade after fabrication, creating a persistent simulation-to-hardware (sim-to-real) gap. When many identicall…

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