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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.09534 |
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| _version_ | 1866913578083155968 |
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| author | Samakovlis, Dimitrios Albini, Stefano Álvarez, Rubén Rodríguez Constantinescu, Denisa-Andreea Schiavone, Pasquale Davide Peón-Quirós, Miguel Atienza, David |
| author_facet | Samakovlis, Dimitrios Albini, Stefano Álvarez, Rubén Rodríguez Constantinescu, Denisa-Andreea Schiavone, Pasquale Davide Peón-Quirós, Miguel Atienza, David |
| contents | Breakthroughs in ultra-low-power chip technology are transforming biomedical wearables, making it possible to monitor patients in real time with devices operating on mere μW. Although many studies have examined the power performance of commercial microcontrollers, it remains unclear which ones perform best across diverse application profiles and which hardware features are most crucial for minimizing energy consumption under varying computational loads. Identifying these features for typical wearable applications and understanding their effects on performance and energy efficiency are essential for optimizing deployment strategies and informing future hardware designs. In this work, we conduct an in-depth study of state-of-the-art (SoA) micro-controller units(MCUs) in terms of processing capability and energy efficiency using representative end-to-end SoA wearable applications. We systematically benchmark each platform across three primary application phases: idle, data acquisition, and processing, allowing a holistic assessment of the platform processing capability and overall energy efficiency across varying patient-monitoring application profiles. Our detailed analysis of performance and energy discrepancies across different platforms reveals key strengths and limitations of the current low-power hardware design and pinpoints the strengths and weaknesses of SoA MCUs. We conclude with actionable insights for wearable application designers and hardware engineers, aiming to inform future hardware design improvements and support optimal platform selection for energy-constrained biomedical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09534 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling Efficient Wearables: An Analysis of Low-Power Microcontrollers for Biomedical Applications Samakovlis, Dimitrios Albini, Stefano Álvarez, Rubén Rodríguez Constantinescu, Denisa-Andreea Schiavone, Pasquale Davide Peón-Quirós, Miguel Atienza, David Signal Processing Breakthroughs in ultra-low-power chip technology are transforming biomedical wearables, making it possible to monitor patients in real time with devices operating on mere μW. Although many studies have examined the power performance of commercial microcontrollers, it remains unclear which ones perform best across diverse application profiles and which hardware features are most crucial for minimizing energy consumption under varying computational loads. Identifying these features for typical wearable applications and understanding their effects on performance and energy efficiency are essential for optimizing deployment strategies and informing future hardware designs. In this work, we conduct an in-depth study of state-of-the-art (SoA) micro-controller units(MCUs) in terms of processing capability and energy efficiency using representative end-to-end SoA wearable applications. We systematically benchmark each platform across three primary application phases: idle, data acquisition, and processing, allowing a holistic assessment of the platform processing capability and overall energy efficiency across varying patient-monitoring application profiles. Our detailed analysis of performance and energy discrepancies across different platforms reveals key strengths and limitations of the current low-power hardware design and pinpoints the strengths and weaknesses of SoA MCUs. We conclude with actionable insights for wearable application designers and hardware engineers, aiming to inform future hardware design improvements and support optimal platform selection for energy-constrained biomedical applications. |
| title | Enabling Efficient Wearables: An Analysis of Low-Power Microcontrollers for Biomedical Applications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2411.09534 |