32 citations · 209 across the 24 of their papers we have counts for
7 papers · 1 filter
Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers
Jakob Schubert, Maximilian Kasper, Maximilian Linke +5
We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs). In mobile applications where…
Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization
Lucas Heublein, Mark Deutel, Axel Plinge +1
Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw in-phase and quadrature (IQ)…
Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
Nina Brolich, Simon Geis, Maximilian Kasper +3
Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species are ideal subjects for this due to thei…
Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
Alexander Mattick, Maniraman Periyasamy, Christian Ufrecht +4
Quantum computing is currently strongly limited by the impact of noise, in particular introduced by the application of two-qubit gates. For this reason, reducing the number of two-…
C-MCTS: Safe Planning with Monte Carlo Tree Search
Dinesh Parthasarathy, Georgios Kontes, Axel Plinge +1
The Constrained Markov Decision Process (CMDP) formulation allows to solve safety-critical decision making tasks that are subject to constraints. While CMDPs have been extensively…
Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving
Sebastian Rietsch, Shih-Yuan Huang, Georgios Kontes +2
Reinforcement learning (RL) has shown to reach super human-level performance across a wide range of tasks. However, unlike supervised machine learning, learning strategies that gen…
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies
Lukas M. Schmidt, Sebastian Rietsch, Axel Plinge +2
Autonomous driving has the potential to revolutionize mobility and is hence an active area of research. In practice, the behavior of autonomous vehicles must be acceptable, i.e., e…