Summary of Selective Task Offloading For Maximum Inference Accuracy and Energy Efficient Real-time Iot Sensing Systems, by Abdelkarim Ben Sada et al.
Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems
by Abdelkarim Ben Sada, Amar Khelloufi, Abdenacer Naouri, Huansheng Ning, Sahraoui Dhelim
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 tackles the challenge of deploying AI models on edge devices, where limited resources pose significant hurdles. To overcome these limitations, the authors propose a dynamic system that allocates inference models to jobs or offloads them to an edge server based on current resource conditions. This problem is formulated as an instance of the unbounded multidimensional knapsack problem, which is strongly NP-hard. To solve this problem efficiently, the authors develop a lightweight hybrid genetic algorithm (LGSTO) that incorporates termination conditions, neighborhood exploration techniques, and various reproduction methods, including NSGA-II. The proposed LGSTO outperforms comparable schemes by 3 times in terms of speed while achieving higher average accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes AI models work better on small devices like smartphones or smart home gadgets. These devices have limited resources like memory and power, which makes it hard to use big AI models that require a lot of resources. The authors came up with a new way to decide when to use different AI models for different tasks and when to send them to the cloud for processing. This helps make sure AI works accurately while also saving energy and time. |
Keywords
* Artificial intelligence * Inference
