Summary of Ilash: a Predictive Neural Architecture Search Framework For Multi-task Applications, by Md Hafizur Rahman et al.
ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications
by Md Hafizur Rahman, Md Mashfiq Rizvee, Sumaiya Shomaji, Prabuddha Chakraborty
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed ILASH (Intelligent Layer-sharing Architecture) paradigm is a novel approach for minimizing power utilization, increasing frame rate, and reducing model size in multi-tasking AI applications deployed on resource-constrained edge devices. This framework leverages layer sharing to create efficient neural network models that can be used for various tasks while meeting specific device constraints. Additionally, ILASH-NAS (Neural Network Architecture Search) is a novel framework that utilizes a data-driven approach to efficiently construct these neural networks. Extensive evaluations using four open-source datasets demonstrate the effectiveness of ILASH and ILASH-NAS, showing significant improvements in model performance and search efficiency with reduced energy utilization, CO2 emission, and training/search time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is used in many fields like healthcare and self-driving cars. Many AI tasks need to do multiple things at once, like analyze pictures and recognize objects. This can be challenging for devices that have limited power or storage space. To solve this problem, researchers propose a new way of building neural networks called ILASH (Intelligent Layer-sharing Architecture). It helps make AI models more efficient by sharing layers, which reduces the need for powerful devices. They also developed a framework to find the best model for a specific task and device constraint. The results show that ILASH is much better than other methods at finding good models quickly and with less energy consumption. |
Keywords
» Artificial intelligence » Neural network
