Samenvatting
Wearable solutions based on Deep Learning (DL) for real-time ECG monitoring are a promising alternative to detect life-threatening arrhythmias. However, DL models suffer of a large memory footprint, which hampers their adoption in portable technologies. Therefore, we leverage a hardware-oriented pruning approach to effectively shrink DL models. We demonstrate that tiny DL models can be reduced to 5.55x (pruning), and 26.6x (pruning+quantization) compression rate, with 82.9% FLOP's reduction. These ultra-compressed models are able to effectively classify life-threatening arrhythmias with minimal or no loss of performance compared with their non-pruned counterparts, which can pave the path towards DL-based biomedical portable solutions.
Originele taal-2 | Engels |
---|---|
Titel | 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Aantal pagina's | 6 |
ISBN van elektronische versie | 978-1-7281-7204-0 |
DOI's | |
Status | Gepubliceerd - 23 dec. 2021 |
Evenement | 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 - Virtual, Online, Berlin, Virtual, Online, Duitsland Duur: 6 okt. 2021 → 9 okt. 2021 https://2021.ieee-biocas.org/ |
Congres
Congres | 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 |
---|---|
Verkorte titel | BioCAS |
Land/Regio | Duitsland |
Stad | Virtual, Online |
Periode | 6/10/21 → 9/10/21 |
Internet adres |
Bibliografische nota
Publisher Copyright:© 2021 IEEE.
Financiering
This work is funded by Marie Sklodowska - Curie Actions