For all autonomous devices the development of resource-aware machine learning techniques is required to reduce the tremendous resource consumption. This work provides theoretical and practical building blocks to bring full-fledged machine learning pipelines to systems with very low computational power or highly restricted energy supply. The presentation of theoretical methods is accompanied by actual learning results on ultra-low-power hardware.
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