Summary of Nash: Neural Architecture Search For Hardware-optimized Machine Learning Models, by Mengfei Ji et al.
NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models
by Mengfei Ji, Yuchun Chang, Baolin Zhang, Zaid Al-Ars
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper introduces NASH, a novel approach that applies neural architecture search to machine learning hardware. The goal is to achieve better trade-offs between high accuracy, high throughput, and low latency in ML algorithms deployed in various applications. Using NASH, four different strategies are presented, all of which show higher accuracy than the original models. The approach can be applied to convolutional neural networks (CNNs), selecting specific model operations to guide training towards higher accuracy. Experimental results demonstrate a top 1 accuracy increase of up to 3.1% and a top 5 accuracy increase of up to 2.2% compared to non-NASH versions on the ImageNet dataset using ResNet18 or ResNet34 models. The approach is also integrated into the FINN hardware model synthesis tool, achieving a maximum throughput of 324.5 fps. NASH models also result in better trade-offs between accuracy and hardware resource utilization, with an accuracy-hardware Pareto curve showing that the four NASH versions represent the best trade-offs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making machine learning algorithms faster and more accurate. It introduces a new way to do this called NASH, which helps find the right combination of neural network operations to get better results. The team tested this approach on different types of neural networks and found that it can improve accuracy by up to 3% compared to usual methods. They also showed how this approach can be used with special hardware tools to make things even faster. |
Keywords
* Artificial intelligence * Machine learning * Neural network
