3 citations · 4 across the 4 of their papers we have counts for
4 papers
Toward a Modular Architecture for Embedded AI Agent Systems at the Edge
Marcus Rüb, Michael Gerhards
The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments rema…
DRIP: DRop unImportant data Points -- Enhancing Machine Learning Efficiency with Grad-CAM-Based Real-Time Data Prioritization for On-Device Training
Marcus Rüb, Daniel Konegen, Patrick Selle +2
Selecting data points for model training is critical in machine learning. Effective selection methods can reduce the labeling effort, optimize on-device training for embedded syste…
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…