BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables

TL;DR

BiomedBench is an open-source benchmark suite for evaluating low-power TinyML biomedical applications on wearable devices, addressing the lack of standardized hardware assessment in this domain.

Contribution

It introduces a comprehensive benchmark suite for TinyML biomedical wearables and evaluates five platforms, highlighting their limitations across diverse applications.

Findings

Modern platforms cannot efficiently handle all biomedical application types.

BiomedBench standardizes hardware evaluation for TinyML biomedical wearables.

Open-source release promotes better hardware and application design.

Abstract

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.