  A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU
to enable non-invasive and comfortable monitoring, but ensuring robust
PPG-based heart-rate monitoring in the presence of motion artifacts is still an
open challenge. Recent state-of-the-art algorithms combine PPG and inertial
signals to mitigate the effect of motion artifacts. However, these approaches
suffer from limited generality. Moreover, their deployment on MCU-based edge
nodes has not been investigated. In this work, we tackle both the
aforementioned problems by proposing the use of hardware-friendly Temporal
Convolutional Networks (TCN) for PPG-based heart estimation. Starting from a
single "seed" TCN, we leverage an automatic Neural Architecture Search (NAS)
approach to derive a rich family of models. Among them, we obtain a TCN that
outperforms the previous state-of-the-art on the largest PPG dataset available
(PPGDalia), achieving a Mean Absolute Error (MAE) of just 3.84 Beats Per Minute
(BPM). Furthermore, we tested also a set of smaller yet still accurate (MAE of
5.64 - 6.29 BPM) networks that can be deployed on a commercial MCU (STM32L4)
which require as few as 5k parameters and reach a latency of 17.1 ms consuming
just 0.21 mJ per inference.
