Summary of Biomedbench: a Benchmark Suite Of Tinyml Biomedical Applications For Low-power Wearables, by Dimitrios Samakovlis et al.
BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
by Dimitrios Samakovlis, Stefano Albini, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu, Pasquale Davide Schiavone, Miguel Peón Quirós, David Atienza
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 new benchmark suite called BiomedBench is proposed for evaluating low-power wearables in the biomedical domain, which has seen significant advancements in chip manufacturing and real-time monitoring of patients using machine learning (ML). The current lack of a systematic approach to hardware evaluation hinders further progress. BiomedBench addresses this gap by providing complete end-to-end TinyML applications for wearable devices, considering various requirements during signal acquisition and processing phases. Additionally, the energy efficiency of five state-of-the-art platforms is evaluated, revealing that modern platforms cannot effectively target all biomedical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BiomedBench is a new way to test wearables that track health in real-time using tiny machine learning (ML). Currently, it’s hard to compare how well different wearable devices do this task because there isn’t a standard way to measure their performance. BiomedBench helps fix this problem by providing many examples of real-world applications for wearable devices and showing how they work together with the device’s hardware. |
Keywords
» Artificial intelligence » Machine learning
