Summary of Optimizing Tinyml: the Impact Of Reduced Data Acquisition Rates For Time Series Classification on Microcontrollers, by Riya Samanta et al.
Optimizing TinyML: The Impact of Reduced Data Acquisition Rates for Time Series Classification on Microcontrollers
by Riya Samanta, Bidyut Saha, Soumya K. Ghosh, Ram Babu Roy
First submitted to arxiv on: 17 Sep 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 The paper explores the effects of reducing data acquisition rates on Tiny Machine Learning (TinyML) models for time series classification, targeting resource-constrained IoT devices. By lowering sampling frequencies, the authors aim to minimize computational demands, RAM usage, energy consumption, and latency while maintaining accuracy. Experiments with six benchmark datasets demonstrate that reduced data rates significantly decrease energy consumption and computation load, with minimal accuracy loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how reducing the amount of data collected affects TinyML models for identifying patterns in time series data, focusing on small devices like those used in the Internet of Things (IoT). By taking fewer measurements, the authors hope to make their models use less energy and memory while still being accurate. They test this idea with six different datasets and find that it helps reduce energy consumption and computation needs without sacrificing too much accuracy. |
Keywords
» Artificial intelligence » Classification » Machine learning » Time series
