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 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.
Learning Hardware-Friendly Classifiers through Algorithmic Stability
Oneto Luca;
2016-01-01
Abstract
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 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
