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

新的LCAP框架弥合了光子神经网络的仿真到硬件鸿沟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一个名为“基于探针的潜在芯片自适应”（LCAP）的新框架，以解决光子神经网络（PNN）中仿真到硬件的鸿沟问题。该方法将硬件自适应分解为可迁移的群体校正和个性化的潜在校正。通过学习历史芯片数据并使用少量输出探针，LCAP可以显著提高未见芯片的准确性，在仿真中将最差设备准确性从89.18%提高到90.54%。 AI

影响提高了光子神经网络的准确性并降低了校准成本，可能实现更高效的硬件部署。

排序理由该集群包含一篇详细介绍光子神经网络新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tianyu Gao, Guantian Zheng · 2026-09-16 04:00

LCAP：面向光子神经网络的基于人口信息和少量输出探针的潜在芯片自适应

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…

本文如何被排名

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LCAP：面向光子神经网络的基于人口信息和少量输出探针的潜在芯片自适应

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