7 papers
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…
Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios
Nisha L. Raichur, Lucas Heublein, Dominik Seuà +2
Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity. Robust RF…
Learning Logical Operations for Arbitrary Quantum Error Correction Codes
Nico Meyer, Christopher Mutschler, Dominik Seuà +2
Logical operations are essential for quantum computation within quantum error-correcting codes. However, discovering their physical realizations is challenging, especially for non-…
Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models
Pranav A, Shashank B, Pranav Siddappa +3
Diffusion models are rapidly redefining 3D anomaly detection in point cloud data. As 3D sensing becomes integral to modern manufacturing, reliable anomaly detection is essential fo…
Learning to Concatenate Quantum Codes
Nico Meyer, Christopher Mutschler, Dominik Seuà +2
Concatenating quantum error correction codes scales error correction capability by driving logical error rates down double-exponentially across levels. However, the noise structure…
Pareto Optimal Benchmarking of AI Models on ARM Cortex Processors for Sustainable Embedded Systems
Pranay Jain, Maximilian Kasper, Göran Köber +3
This work presents a practical benchmarking framework for optimizing artificial intelligence (AI) models on ARM Cortex processors (M0+, M4, M7), focusing on energy efficiency, accu…