Summary of Profiling Ai Models: Towards Efficient Computation Offloading in Heterogeneous Edge Ai Systems, by Juan Marcelo Parra-ullauri et al.
Profiling AI Models: Towards Efficient Computation Offloading in Heterogeneous Edge AI Systems
by Juan Marcelo Parra-Ullauri, Oscar Dilley, Hari Madhukumar, Dimitra Simeonidou
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET); Networking and Internet Architecture (cs.NI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research roadmap aims to address the challenges of edge AI by profiling AI models and predicting resource utilization and task completion time. To achieve this, the authors focus on capturing data about model types, hyperparameters, and underlying hardware. The initial experiments with over 3,000 runs show promising results in optimizing resource allocation and enhancing Edge AI performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Edge AI is a crucial technology for future services in 6G networks, allowing users to access AI applications directly on their devices without the need for cloud computing. However, edge AI faces challenges such as limited resources during simultaneous offloads and unrealistic assumptions about homogeneous system architecture. The proposed research roadmap aims to address these challenges by profiling AI models and predicting resource utilization and task completion time. |
