想用更少的样本准确预测模型在手机上的延迟？HiFi-LLP带置信度打分，NAS提速8.6倍。
HiFi-LLP是一种基于图注意力网络的延迟预测器，引入置信度指标来判断预测可靠性。在LatBench数据集的六个设备上，HiFi-LLP的Spearman秩相关系数最高达0.996。相比先前平台特定预测器，它在10%准确率边界上提升最多9个百分点。混合NAS框架将低置信度预测路由至硬件在环测量，实现8.6倍加速同时保持竞争性的Pareto前沿。
HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS
With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential. These methods rely on real feedback from the target hardware to tailor DNN architectures for efficient deployment. While the search can be parallelized, latency measurements via hardware-in-the-loop (HIL) remain a bottleneck due to their sequential nature. Recent approaches use latency predictors to replace costly HIL feedback, but challenges persist: (1) platform-specific predictors often require tens of thousands of samples, and (2) inaccurate predictions can mislead the NAS process. To address this, we introduce HiFi-LLP, a high-fidelity, low-cost latency predictor based on graph attention networks, augmented with a confidence metric. HiFi-LLP outperforms prior platform-specific predictors by up to 9 percentage points (p.p.) in the 10% accuracy bound and achieves a Spearman's rank correlation of up to 0.996 across six devices in the LatBench dataset. We further propose a hybrid NAS framework that routes low-confidence predictions to HIL, achieving up to 8.6$\times$ speedup compared to typical NAS while maintaining a competitive Pareto front.