Summary of Scaling Up Quantization-aware Neural Architecture Search For Efficient Deep Learning on the Edge, by Yao Lu et al.
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge
by Yao Lu, Hiram Rayo Torres Rodriguez, Sebastian Vogel, Nick van de Waterlaat, Pavol Jancura
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors aim to overcome the limitations of current quantization-aware Neural Architecture Search (QA-NAS) methods by introducing a new approach that enables QA-NAS on large-scale tasks. They achieve this by leveraging block-wise NAS, which allows for the search of highly accurate and efficient quantized models. The authors demonstrate their approach’s effectiveness on the semantic segmentation task using the Cityscapes dataset, showing significant reductions in model size and inference time without sacrificing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is important because it can help make AI models more suitable for edge devices. Neural Architecture Search (NAS) has become a popular way to design accurate and efficient networks for these devices. However, most existing approaches only work well on small tasks. The authors’ new approach allows them to search for highly accurate and efficient quantized models that are even smaller and faster than current state-of-the-art models. |
Keywords
* Artificial intelligence * Inference * Quantization * Semantic segmentation
