1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications
Nikhil Pratap Ghanathe, Vivek Seshadri, Rahul Sharma +2
Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices. On one hand, existing ML-to-FPGA compilers…