Researchers have developed HiFi-LLP, a novel latency predictor designed to accelerate hardware-aware neural architecture search (HW-NAS) for deep neural networks on edge devices. This predictor utilizes graph attention networks and incorporates a confidence metric to improve accuracy and reduce the need for extensive hardware-in-the-loop testing. HiFi-LLP demonstrates superior performance compared to existing platform-specific predictors, achieving high correlation and accuracy on the LatBench dataset. A hybrid NAS framework leverages HiFi-LLP's confidence scores to selectively use hardware feedback, leading to significant speedups in the search process while maintaining competitive results. AI
IMPACT Accelerates the deployment of deep neural networks on edge devices by improving the efficiency of hardware-aware optimization techniques.
RANK_REASON The cluster contains a research paper detailing a new method for latency prediction in hardware-aware neural architecture search.
- alphaXiv
- CatalyzeX
- DagsHub
- Deep Neural Networks
- Gotit.pub
- Graph Attention Networks
- HiFi-LLP
- Hugging Face
- HW-NAS
- IArxiv
- LatBench
- ScienceCast
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