Summary of Pareto Data Framework: Steps Towards Resource-efficient Decision Making Using Minimum Viable Data (mvd), by Tashfain Ahmed and Josh Siegel
Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)
by Tashfain Ahmed, Josh Siegel
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 introduces the Pareto Data Framework, an approach for identifying Minimum Viable Data (MVD) that enables machine learning applications on constrained platforms like embedded systems, mobile devices, and IoT devices. The framework optimizes efficiency by reducing bandwidth, energy, computation, and storage costs without sacrificing performance. By strategically selecting MVD, it addresses common inefficient practices in IoT applications, such as overprovisioning of sensors and oversampling of signals. This approach can maintain high performance with reduced data rates (up to 75%) and bit depths (down to 50%), leading to significant cost and resource savings. The paper demonstrates the effectiveness of this framework through an experimental methodology that characterizes acoustic data after downsampling, quantization, and truncation, resulting in substantial reductions without sacrificing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better use of data on devices like smartphones and smart home appliances. It shows how to find the most important information we need for machine learning tasks while using less energy, bandwidth, and storage space. This is important because many devices are limited by these resources. The authors test their approach with audio data and show that it can work well even when the data is reduced in quality. This could help people develop more efficient devices and applications, making advanced AI technologies more accessible to a wider range of people and industries. |
Keywords
* Artificial intelligence * Machine learning * Quantization
