BiomedBench

Research Line

Personalized health

Keywords
Biomedical, Benchmarks, Ultra-Low Power, MCU, Wearables

Team

  Albini Stefano
  Atienza Alonso David
  Constantinescu Denisa-Andreea
  Rodriguez Álvarez Rubén
  Samakovlis Dimitrios


The design of low-power wearables for the biomedical domain has received much attention in recent decades, as technological advancements in chip manufacturing have allowed real-time monitoring of patients within the μW range. To ensure continued progression in this domain, a co-design view that optimizes both hardware and software simultaneously is necessary.

In this work, we propose BiomedBench, a new benchmark suite composed of complete biomedical applications for real-time monitoring of patients using wearable devices. Each application presents different requirements during the typical signal acquisition and processing phases, including varying computational workloads and relations between active and idle times.

Therefore, BiomedBench provides hardware developers with a tool to assess the efficiency of their ultra-low power (ULP) platform designs under varying requirements. Furthermore, we evaluated five state-of-the-art ULP platforms in terms of energy efficiency and performance using BiomedBench. This evaluation shows that current ULP platforms cannot effectively target all types of different biomedical applications. As a result, BiomedBench also allows system designers to identify the most suitable platform for their software applications.

To this end, BiomedBench will be released as an open-source suite to enable future improvements in the entire domain of bioengineering systems and application design.

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Related Publications

BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
Samakovlis, Dimitrios; Albini, Stefano; Rodr�guez �lvarez, Rub�n; Constantinescu, Denisa-Andreea; Schiavone, Pasquale Davide; Peon Quiros, Miguel; Atienza Alonso, David
2024-10-27IEEE Design & TestPublication funded by SwissChips (SwissChips - State Secretariat for Education, Research and Innovation)Publication funded by ACCESS ()Publication funded by Personalized Detection of Epileptic Seizure in the Internet of Things (IoT) Era (Sinergia – interdisciplinary, collaborative and breakthrough)