Summary of Micronas: Zero-shot Neural Architecture Search For Mcus, by Ye Qiao et al.
MicroNAS: Zero-Shot Neural Architecture Search for MCUs
by Ye Qiao, Haocheng Xu, Yifan Zhang, Sitao Huang
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed MicroNAS framework is a zero-shot Neural Architecture Search (NAS) method designed for microcontroller units (MCUs) in edge computing. This approach differs from previous methods that required extensive training on super networks or architecture evaluations, making them impractical for real-world applications. Instead, MicroNAS considers the target hardware optimality during the search process, utilizing specialized performance indicators to identify optimal neural architectures without high computational costs. The framework achieves a significant improvement in search efficiency, discovering models with over 3.23x faster MCU inference while maintaining similar accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MicroNAS is a new way to find the best type of computer brain (called a neural architecture) for small computers called microcontrollers. These small computers are used in things like smart home devices and cars. The problem is that finding the best brain takes a lot of computing power, which isn’t practical for these small computers. MicroNAS solves this by using special clues to find the best brain quickly, without needing lots of computing power. This new way of finding brains is much faster and better than before, allowing small computers to work more efficiently. |
Keywords
* Artificial intelligence * Inference * Zero shot
