3 papers
cs.LG2026
Trainable Bitwise Soft Quantization for Input Feature Compression
Karsten Schrödter, Jan Stenkamp, Nina Herrmann +1
The growing demand for machine learning applications in the context of the Internet of Things calls for new approaches to optimize the use of limited compute and memory resources.…
cs.LG2026
Boosted Trees on a Diet: Compact Models for Resource-Constrained Devices
Nina Herrmann, Jan Stenkamp, Benjamin Karic +2
Deploying machine learning models on compute-constrained devices has become a key building block of modern IoT applications. In this work, we present a compression scheme for boost…
cs.LG2025
Send Less, Save More: Energy-Efficiency Benchmark of Embedded CNN Inference vs. Data Transmission in IoT
Benjamin Karic, Nina Herrmann, Jan Stenkamp +3
The integration of the Internet of Things (IoT) and Artificial Intelligence offers significant opportunities to enhance our ability to monitor and address ecological changes. As en…