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20212024
most citedT-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit

1 citations · 1 across the 3 of their papers we have counts for

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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…