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cs.LG2024
DEBUG-HD: Debugging TinyML models on-device using Hyper-Dimensional computing
Nikhil P Ghanathe, Steven J E Wilton
TinyML models often operate in remote, dynamic environments without cloud connectivity, making them prone to failures. Ensuring reliability in such scenarios requires not only dete…
cs.LG2024
QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring
Nikhil P Ghanathe, Steven J E Wilton
Uncertainty quantification (UQ) provides a resource-efficient solution for on-device monitoring of tinyML models deployed without access to true labels. However, existing UQ method…
cs.LG2022★ 1 cited
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit
Nikhil P Ghanathe, Steve Wilton
Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML research…