nanoML for Human Activity Recognition
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
Human Activity Recognition (HAR) is critical for applications in healthcare,
fitness, and IoT, but deploying accurate models on resource-constrained devices
remains challenging due to high energy and memory demands. This paper
demonstrates the application of Differentiable Weightless Neural Networks
(DWNs) to HAR, achieving competitive accuracies of 96.34% and 96.67% while
consuming only 56nJ and 104nJ per sample, with an inference time of just 5ns
per sample. The DWNs were implemented and evaluated on an FPGA, showcasing
their practical feasibility for energy-efficient hardware deployment. DWNs
achieve up to 926,000x energy savings and 260x memory reduction compared to
state-of-the-art deep learning methods. These results position DWNs as a
nano-machine learning nanoML model for HAR, setting a new benchmark in energy
efficiency and compactness for edge and wearable devices, paving the way for
ultra-efficient edge AI.
