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 predi
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 55%Hardware startup unveils inference accelerator →
- PossiblePossibly related (embedding) · 55%microsoft/onnxruntime →
- PossiblePossibly related (embedding) · 53%fastmachinelearning/hls4ml →
- PossiblePossibly related (embedding) · 50%Tencent/ncnn →
- PossiblePossibly related (embedding) · 49%raullenchai/Rapid-MLX →
- LinkedLinked via arxiv author · 85%Shambhavi Balamuthu Sampath →
“HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS”
- LinkedLinked via arxiv author · 85%Behzad Shomali →
“HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS”
- LinkedLinked via arxiv author · 85%Nael Fasfous →
“HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS”