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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.…
Energy Overhead in Sensor Nodes: Embedded Machine Learning and Data Offloading via Low Power Wide Area Networks
Benjamin Karic, Nina Herrmann, Jan Stenkamp +3
Image- and audio-based remote IoT applications offer a substantial opportunity to address ecological changes. However, to be effective, they must operate for long periods on limite…
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…