Energy-Efficient Adaptive Classifier Design for Mobile Systems

Abstract

With the continuous increase in the amount of data that needs to be processed by digital mobile systems, energy-efficient computation has become a critical design constraint for mobile systems. In this paper, we propose an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification "hardness" across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈ 100× more energy efficient but has ≈ 1% higher error rate than a complex radial basis function classifier and is ≈ 10× less energy efficient but has ≈ 40% lower error rate than a simple linear classifier across a wide range of classification data sets.