LCAP：面向光子神经网络的少输出探针群体信息潜在芯片适配
LCAP: Population-Informed Latent Chip Adaptation from Few Output Probes for Photonic Neural Networks
- City University of Hong Kong（香港城市大学）
- Sichuan University（四川大学）
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
LCAP利用群体信息从少量输出探针推断芯片潜在校正，实现光子神经网络免优化的硬件适配，显著提升部署准确率。
AI中文摘要：
光子神经网络（PNNs）提供高效的模拟推理，但在理想器件模型下优化的参数在制造后可能退化，造成持续的仿真到硬件（sim-to-real）差距。当部署许多设计相同的芯片时，从零开始校准每个器件会加剧这一成本。我们提出潜在芯片探针适配（LCAP），一种群体信息框架，将硬件适配分解为可迁移的群体校正和探针推断的潜在个性化。LCAP首先从80个历史芯片学习共享校正，然后从器件特定细化中提取低维校正空间。部署时，32个固定无标签输出探针推断未见芯片的潜在校正坐标，实现无需目标器件优化的前馈个性化。在具有相位变化、分束器误差、量化和串扰的三层64模式MZI模拟器上，准确率从直接部署下的80.4147%提升到共享校准后的92.6860%，以及LCAP下的93.3617%。LCAP改善了27/30个未见芯片，并将最差器件准确率从89.18%提升到90.54%。
英文摘要：
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 identically designed chips are deployed, calibrating each device from scratch compounds this cost. We propose Latent Chip Adaptation from Probes (LCAP), a population-informed framework that decomposes hardware adaptation into a transferable population correction and probe-inferred latent personalization. LCAP first learns a shared correction from 80 historical chips, then extracts a low-dimensional correction space from device-specific refinements. At deployment, 32 fixed unlabeled output probes infer an unseen chip's latent correction coordinates, enabling feed-forward personalization without target-device optimization. On a three-layer 64-mode MZI simulator with phase variation, beam-splitter errors, quantization, and crosstalk, accuracy improves from 80.4147% under direct deployment to 92.6860% after shared calibration and 93.3617% with LCAP. LCAP improves 27/30 unseen chips and raises worst-device accuracy from 89.18% to 90.54%.
