8 citations · 12 across the 3 of their papers we have counts for
3 papers
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption
Marcus Rüb, Philipp Tuchel, Axel Sikora +1
A new algorithm for incremental learning in the context of Tiny Machine learning (TinyML) is presented, which is optimized for low-performance and energy efficient embedded devices…
Advancing On-Device Neural Network Training with TinyPropv2: Dynamic, Sparse, and Efficient Backpropagation
Marcus Rüb, Axel Sikora, Daniel Mueller-Gritschneder
This study introduces TinyPropv2, an innovative algorithm optimized for on-device learning in deep neural networks, specifically designed for low-power microcontroller units. TinyP…
TinyProp -- Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning
Marcus Rüb, Daniel Maier, Daniel Mueller-Gritschneder +1
Training deep neural networks using backpropagation is very memory and computationally intensive. This makes it difficult to run on-device learning or fine-tune neural networks on…