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