Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms
for Cardiac Arrhythmia Detection
- URL: http://arxiv.org/abs/2001.03538v1
- Date: Wed, 8 Jan 2020 10:35:48 GMT
- Title: Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms
for Cardiac Arrhythmia Detection
- Authors: Antonino Faraone, Ricard Delgado-Gonzalo
- Abstract summary: We focus on the inference of neural networks running in microcontrollers and low-power processors.
We adapted an existing convolutional-recurrent neural network to detect and classify cardiac arrhythmias.
We show our implementation in fixed-point precision, using the CMSIS-NN libraries, with a memory footprint of 195.6KB, and a throughput of 33.98MOps/s.
- Score: 0.18459705687628122
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Low-power sensing technologies, such as wearables, have emerged in the
healthcare domain since they enable continuous and non-invasive monitoring of
physiological signals. In order to endow such devices with clinical value,
classical signal processing has encountered numerous challenges. However,
data-driven methods, such as machine learning, offer attractive accuracies at
the expense of being resource and memory demanding. In this paper, we focus on
the inference of neural networks running in microcontrollers and low-power
processors which wearable sensors and devices are generally equipped with. In
particular, we adapted an existing convolutional-recurrent neural network,
designed to detect and classify cardiac arrhythmias from a single-lead
electrocardiogram, to the low-power embedded System-on-Chip nRF52 from Nordic
Semiconductor with an ARM's Cortex-M4 processing core. We show our
implementation in fixed-point precision, using the CMSIS-NN libraries, yields a
drop of $F_1$ score from 0.8 to 0.784, from the original implementation, with a
memory footprint of 195.6KB, and a throughput of 33.98MOps/s.
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