Summary of Monas: Efficient Zero-shot Neural Architecture Search For Mcus, by Ye Qiao et al.
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs
by Ye Qiao, Haocheng Xu, Yifan Zhang, Sitao Huang
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes MONAS, a novel hardware-aware zero-shot Neural Architecture Search (NAS) framework specifically designed for microcontroller units (MCUs) in edge computing. MONAS incorporates hardware optimality considerations into the search process through its proposed MCU hardware latency estimation model and specialized performance indicators (proxies). This approach enables optimal neural architectures to be discovered without heavy training and evaluation costs, optimizing for both hardware latency and accuracy under resource constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MONAS is a new way to find the best computer networks for tiny computers called microcontrollers. These little computers are used in things like smart home devices or robots. The old way of doing this took too long and was expensive. MONAS makes it faster and cheaper by thinking about how fast each microcontroller can work, so it picks the right network that fits each one’s abilities. |
Keywords
» Artificial intelligence » Zero shot
