Articles | Volume 14, issue 2
https://doi.org/10.5194/jsss-14-169-2025
https://doi.org/10.5194/jsss-14-169-2025
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11 Aug 2025
Regular research article | Highlight paper |  | 11 Aug 2025

Efficient hardware implementation of interpretable machine learning based on deep neural network representations for sensor data processing

Julian Schauer, Payman Goodarzi, Andreas Schütze, and Tizian Schneider

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Short summary
Robust and interpretable machine learning algorithms, which have been proven in various applications, often lack efficient implementation on limited hardware. The novel approach is to convert the inference of the interpretable ML into a deep neural network, which is efficiently executable on edge hardware. This approach was validated in terms of runtime efficiency, memory requirements, and accuracy and resulted in a significant improvement in terms of runtime and memory requirements.
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