Summary of Memory-optimized Once-for-all Network, by Maxime Girard et al.
Memory-Optimized Once-For-All Network
by Maxime Girard, Victor Quétu, Samuel Tardieu, Van-Tam Nguyen, Enzo Tartaglione
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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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 abstract discusses the challenges of deploying Deep Neural Networks (DNNs) on different hardware platforms due to varying resource constraints. Researchers have employed handcrafted approaches and Neural Architecture Search (NAS) methods, such as Once-For-All (OFA), to craft efficient DNNs without sacrificing performance. However, OFA focuses on limiting maximum memory usage per layer, leaving room for unexploited potential in model generalizability. This paper introduces Memory-Optimized OFA (MOOFA), designed to enhance DNN deployment on resource-limited devices by maximizing memory usage and features diversity across different configurations. The MOOFA supernet is tested on ImageNet, demonstrating improvements in memory exploitation and model accuracy compared to the original OFA supernet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about making computers smarter by using special computer programs called Deep Neural Networks (DNNs). These programs need to work well on many different kinds of computers, which can be tricky. Scientists have come up with ways to make these programs more efficient without sacrificing their ability to learn and do tasks. One way is to use something called Once-For-All (OFA), which lets the computer find a good version of the program that works well. However, this approach doesn’t always take advantage of all the memory available on the computer. This new paper introduces an improved version called Memory-Optimized OFA (MOOFA) that can use more memory and make the programs even better. |
