  Personalized ubiquitous healthcare solutions require energy-efficient
wearable platforms that provide an accurate classification of bio-signals while
consuming low average power for long-term battery-operated use. Single lead
electrocardiogram (ECG) signals provide the ability to detect, classify, and
even predict cardiac arrhythmia. In this paper, we propose a novel temporal
convolutional network (TCN) that achieves high accuracy while still being
feasible for wearable platform use. Experimental results on the ECG5000 dataset
show that the TCN has a similar accuracy (94.2%) score as the state-of-the-art
(SoA) network while achieving an improvement of 16.5% in the balanced accuracy
score. This accurate classification is done with 27 times fewer parameters and
37 times less multiply-accumulate operations. We test our implementation on two
publicly available platforms, the STM32L475, which is based on ARM Cortex M4F,
and the GreenWaves Technologies GAP8 on the GAPuino board, based on 1+8 RISC-V
CV32E40P cores. Measurements show that the GAP8 implementation respects the
real-time constraints while consuming 0.10 mJ per inference. With 9.91
GMAC/s/W, it is 23.0 times more energy-efficient and 46.85 times faster than an
implementation on the ARM Cortex M4F (0.43 GMAC/s/W). Overall, we obtain 8.1%
higher accuracy while consuming 19.6 times less energy and being 35.1 times
faster compared to a previous SoA embedded implementation.
