Modular Design and Optimization of Biomedical Applications for Ultralow Power Heterogeneous Platforms

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems 2020-11, Vol.39 (11), p.3821-3832
• Main Authors: De Giovanni, Elisabetta, Montagna, Fabio, Denkinger, Benoit W., Machetti, Simone, Peon-Quiros, Miguel, Benatti, Simone, Rossi, Davide, Benini, Luca, Atienza, David
• Format: Article
• Language: English
• Subjects: Acceleration; Accelerator architectures; Accelerators; Biomedical materials; biomedical monitoring; Classifiers; Design optimization; edge computing; Electrocardiography; Energy consumption; Energy management; Kernel; Low-power electronics; Medical electronics; Memory management; Modular design; multi-core processing; Optimization techniques; Parallel processing; Platforms; Power management; Remote monitoring; Sensors; Signal analysis; Signal processing; wearable sensors; Wearable technology; Wireless sensor networks
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2020.3012652

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable sensors have evolved into multicore systems with advanced power-saving capabilities and additional heterogeneous components. In this article, we present an approach that applies optimization and parallelization techniques uncovered by modern ultralow power (ULP) platforms in the SW layers with the goal of improving the mapping and reducing the energy consumption of biomedical applications. Additionally, we investigate the benefit of integrating domain-specific accelerators to further reduce the energy consumption of the most computationally expensive kernels. Using 30-s excerpts of signals from two public databases, we apply the proposed optimization techniques on well-known modules of biomedical benchmarks from the state-of-the-art and two complete applications. We observe speed-ups of 5.17{\times } and energy savings of 41.6% for the multicore implementation using a cluster of 8 cores with respect to single-core wearable sensor designs when processing a standard 12-lead electrocardiogram (ECG) signal analysis. Additionally, we conclude that the minimum workload required to take advantage of parallelization for a heartbeat classifier corresponds to the processing of 3-lead ECG signals, with a speed-up of 2.96{\times } and energy savings of 19.3%. Moreover, we observe additional energy savings of up to 7.75% and 16.8% by applying power management and memory scaling to the multicore implementation of the 3-lead beat classifier and 12-lead ECG analysis, respectively. Finally, by integrating hardware (HW) acceleration we observe overall energy savings of up to 51.3% for the 12-lead ECG analysis.