Details
Presenter(s)
![Thorir Mar Ingolfsson Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/11031.jpg?h=fbf7a813&itok=Xf9UMu0e)
Display Name
Thorir Mar Ingolfsson
- Affiliation
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AffiliationETH Zürich
- Country
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CountrySwitzerland
Abstract
We propose a novel temporal convolutional network for cardiac arrhythmia detection that achieves 16.5% higher balanced accuracy than the SoA network, with 27x fewer parameters and 37x less operations. We implement our model on two platforms, the STM32L475 with ARM Cortex-M4F, and the GreenWavesTechnologies GAP8 with 1+8 RISC-V CV32E40P cores. Measurements show that GAP8 implementation respects real-time constraints while consuming 0.10mJ/inference. With 9.91 GMAC/s/W, it is 23x more energy-efficient and 46.85x faster than an ARM Cortex-M4F implementation. Overall, we obtain 8.1% higher accuracy while consuming 19.6x less energy and being 35.1x faster compared to a previous SoA embedded implementation.