Summary of Task-oriented Real-time Visual Inference For Iovt Systems: a Co-design Framework Of Neural Networks and Edge Deployment, by Jiaqi Wu et al.
Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment
by Jiaqi Wu, Simin Chen, Zehua Wang, Wei Chen, Zijian Tian, F. Richard Yu, Victor C. M. Leung
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 co-design framework optimizes neural network architecture and deployment strategies to improve the computational performance of edge devices in Internet of Video Things (IoVT) systems. The approach implements a dynamic model structure based on re-parameterization and Roofline-based model partitioning strategy to enhance throughput. A multi-objective co-optimization approach balances throughput and accuracy, while mathematical consistency and convergence of partitioned models are derived. Experimental results show significant improvements in throughput (12.05% on MNIST, 18.83% on ImageNet) and superior classification accuracy compared to baseline algorithms. The method achieves stable performance across different devices, underscoring its adaptability for high-accuracy, real-time detection in IoVT systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research paper, scientists developed a new way to make computers that analyze images work faster and better on devices with limited power. This is important because there are more pictures being taken and analyzed all the time. They created a special framework that helps decide what kind of computer program to use for each image and how to divide it up into smaller parts so it can be done quickly and accurately. |
Keywords
» Artificial intelligence » Classification » Neural network » Optimization
