Details
Poster
Presenter(s)
![Lizeth Gonzalez-Carabarin Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/70741.png?h=83a548ea&itok=ufj5Fz3V)
Display Name
Lizeth Gonzalez-Carabarin
- Affiliation
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AffiliationEindhoven University of Technology
- Country
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CountryNetherlands
Abstract
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.