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Learning Hardware-Friendly Classifiers Through Algorithmic Stability
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DOI：10.1145/2836165.png)
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Abstract
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Most state-of-the-art machine-learning (ML) algorithms do not consider the computational constraints of implementing the learned model on embedded devices. These constraints are, for example, the limited depth of the arithmetic unit, the memory availability, or the battery capacity. We propose a new learning framework, the Algorithmic Risk Minimization (ARM), which relies on Algorithmic-Stability, and includes these constraints inside the learning process itself. ARM allows one to train advanced resource-sparing ML models and to efficiently deploy them on smart embedded systems. Finally, we show the advantages of our proposal on a smartphone-based Human Activity Recognition application by comparing it to a conventional ML approach.
Keywords:
Support vector machines
hardware-friendly classifiers
bit-based classifiers
generalization performances
algorithmic stability
rademacher complexity
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