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

• URL: http://arxiv.org/abs/2609.16823v1
• Date: Tue, 15 Sep 2026 08:25:14 GMT
• Title: LCAP: Population-Informed Latent Chip Adaptation from Few Output Probes for Photonic Neural Networks
• Abstract summary: Photonic neural networks (PNNs) offer efficient analog inference, but parameters under ideal device models can degrade after fabrication.<n>We propose Latent Chip Adaptation from Probes (LCAP), a framework that decomposes hardware adaptation into a transferable population correction and probe-inferred latent personalization.
• Score: 6.982312589287921
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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%.

Related papers

• A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability [10.86517852984593]
  We present a unified reinforcement-learning framework that discovers compact parametrized quantum circuits (PQCs) for data-scarce device modeling.<n>A graph neural network (GNN) policy searches circuit architectures using leave-one-group-out cross-validation (LOGOCV) error on held-out process or geometry groups as the reward.<n>The framework achieves the lowest mean absolute error (MAE) on all 11 targets versus six classical baselines, with 59% lower error (Ioff) and 81% tighter fold variability (VTH) for HEMTs and 84% lower error (V
  arXiv  Detail & Related papers  (2026-09-04T15:15:40Z)
• EdgeFlex-Transformer: Transformer Inference for Edge Devices [2.1130318406254074]
  We propose a lightweight yet effective multi-stage optimization pipeline designed to compress and accelerate Vision Transformers (ViTs)<n>Our methodology combines activation profiling, memory-aware pruning, selective mixed-precision execution, and activation-aware quantization (AWQ) to reduce the model's memory footprint without requiring costly retraining or task-specific fine-tuning.<n>Experiments on CIFAR-10 demonstrate that the fully optimized model achieves a 76% reduction in peak memory usage and over 6x lower latency, while retaining or even improving accuracy compared to the original FP32 baseline.
  arXiv  Detail & Related papers  (2025-12-17T21:45:12Z)
• Beyond Outliers: A Study of Optimizers Under Quantization [82.75879062804955]
  We study impact of choice on model robustness under quantization.<n>We evaluate how model performance degrades when trained with different baselines.<n>We derive scaling laws for quantization-aware training under different parameters.
  arXiv  Detail & Related papers  (2025-09-27T21:15:22Z)
• Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification [0.0]
  We present a data-driven Forward Selection with Neighborhood Refinement (FSNR) algorithm to determine the number and placement of Phasor Measurement Units (PMUs)<n>The proposed FSNR-SVM method identifies a minimal PMU configuration that achieves the best overall CNN performance.
  arXiv  Detail & Related papers  (2025-09-02T11:05:58Z)
• Multi-Scale PCB Defect Detection with YOLOv8 Network Improved via Pruning and Lightweight Network [0.0]
  YOLOv8 is able to improve the detection speed and accuracy by optimizing the backbone network, the neck network and the detection head, the loss function and the adaptive pruning rate.<n>On the publicly available PCB defect dataset, mAP0.5 reaches 99.32% and mAP0.5:0.9 reaches 75.18%, which is 10.13% higher compared to YOLOv8n.
  arXiv  Detail & Related papers  (2025-07-23T03:47:00Z)
• Neural Precision Polarization: Simplifying Neural Network Inference with Dual-Level Precision [0.4124847249415279]
  A floating-point model can be trained in the cloud and then downloaded to an edge device. Network weights and activations are directly quantized to meet the edge devices' desired level, such as NF4 or INT8. We show that neural precision polarization enables approximately 464 TOPS per Watt MAC efficiency and reliability.
  arXiv  Detail & Related papers  (2024-11-06T16:02:55Z)
• Test-Time Model Adaptation with Only Forward Passes [68.11784295706995]
  Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. We propose a test-time Forward-Optimization Adaptation (FOA) method. FOA runs on quantized 8-bit ViT, outperforms gradient-based TENT on full-precision 32-bit ViT, and achieves an up to 24-fold memory reduction on ImageNet-C.
  arXiv  Detail & Related papers  (2024-04-02T05:34:33Z)
• The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling [78.6155095947769]
  Skip-Tuning is a simple yet surprisingly effective training-free tuning method on the skip connections. Our method can achieve 100% FID improvement for pretrained EDM on ImageNet 64 with only 19 NFEs (1.75) While Skip-Tuning increases the score-matching losses in the pixel space, the losses in the feature space are reduced.
  arXiv  Detail & Related papers  (2024-02-23T08:05:23Z)
• On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks [52.97107229149988]
  We propose an On-Chip Hardware-Aware Quantization framework, performing hardware-aware mixed-precision quantization on deployed edge devices. For efficiency metrics, we built an On-Chip Quantization Aware pipeline, which allows the quantization process to perceive the actual hardware efficiency of the quantization operator. For accuracy metrics, we propose Mask-Guided Quantization Estimation technology to effectively estimate the accuracy impact of operators in the on-chip scenario.
  arXiv  Detail & Related papers  (2023-09-05T04:39:34Z)
• Non-Parametric Adaptive Network Pruning [125.4414216272874]
  We introduce non-parametric modeling to simplify the algorithm design. Inspired by the face recognition community, we use a message passing algorithm to obtain an adaptive number of exemplars. EPruner breaks the dependency on the training data in determining the "important" filters.
  arXiv  Detail & Related papers  (2021-01-20T06:18:38Z)
• SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
  This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector. It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-07-26T12:32:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.