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QUTE: Quantifying Uncertainty in TinyML models with Early-exit-assisted ensembles
QUTE adds additional output blocks at the final exit of the base network and distills the knowledge of early-exits into these blocks to create a diverse and lightweight ensemble architecture.
DNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization
The imperative to deploy Deep Neural Network (DNN) models on resource-constrained edge devices, spurred by privacy concerns, has become increasingly apparent.
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit
In this paper, we show how such models can be enhanced by the addition of an early exit intermediate classifier.
MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications
Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices.
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