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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2406.03886 |
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| _version_ | 1866912068743987200 |
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| author | Samakovlis, Dimitrios Albini, Stefano Álvarez, Rubén Rodríguez Constantinescu, Denisa-Andreea Schiavone, Pasquale Davide Quirós, Miguel Peón Atienza, David |
| author_facet | Samakovlis, Dimitrios Albini, Stefano Álvarez, Rubén Rodríguez Constantinescu, Denisa-Andreea Schiavone, Pasquale Davide Quirós, Miguel Peón Atienza, David |
| contents | The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despite advances in application and hardware design research, the domain lacks a systematic approach to hardware evaluation. In this work, we propose BiomedBench, a new benchmark suite composed of complete end-to-end TinyML biomedical applications for real-time monitoring of patients using wearable devices. Each application presents different requirements during typical signal acquisition and processing phases, including varying computational workloads and relations between active and idle times. Furthermore, our evaluation of five state-of-the-art low-power platforms in terms of energy efficiency shows that modern platforms cannot effectively target all types of biomedical applications. BiomedBench is released as an open-source suite to standardize hardware evaluation and guide hardware and application design in the TinyML wearable domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03886 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables Samakovlis, Dimitrios Albini, Stefano Álvarez, Rubén Rodríguez Constantinescu, Denisa-Andreea Schiavone, Pasquale Davide Quirós, Miguel Peón Atienza, David Machine Learning Signal Processing The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despite advances in application and hardware design research, the domain lacks a systematic approach to hardware evaluation. In this work, we propose BiomedBench, a new benchmark suite composed of complete end-to-end TinyML biomedical applications for real-time monitoring of patients using wearable devices. Each application presents different requirements during typical signal acquisition and processing phases, including varying computational workloads and relations between active and idle times. Furthermore, our evaluation of five state-of-the-art low-power platforms in terms of energy efficiency shows that modern platforms cannot effectively target all types of biomedical applications. BiomedBench is released as an open-source suite to standardize hardware evaluation and guide hardware and application design in the TinyML wearable domain. |
| title | BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2406.03886 |