Summary of Simulating Battery-powered Tinyml Systems Optimised Using Reinforcement Learning in Image-based Anomaly Detection, by Jared M. Ping and Ken J. Nixon
Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection
by Jared M. Ping, Ken J. Nixon
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A Tiny Machine Learning (TinyML) study optimizes energy consumption in battery-powered image-based anomaly detection Internet of Things (IoT) systems, crucial for smart industry solutions like smart agriculture and healthcare. By extending previous work on on-device inferencing and training, this research uses Reinforcement Learning (RL) to improve the deployment battery life of such systems. Benchmarking simulated battery life effects, it’s shown that RL optimizes system operations, including cloud anomaly processing and on-device training, yielding a 22.86% improvement compared to static approaches and a 10.86% gain over dynamic methods. The proposed solution has a low memory footprint of 800 B, facilitating real-world deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TinyML helps create smart solutions like agriculture and healthcare. This research makes battery-powered systems more efficient by using machine learning. They test different ways to optimize the system’s energy use and find that Reinforcement Learning (RL) works best. It improves battery life by 22.86% compared to other methods! The new solution only needs a small amount of memory, making it easy to use in real-world applications. |
Keywords
* Artificial intelligence * Anomaly detection * Machine learning * Reinforcement learning
