Low-Energy architectures of linear classifiers for IoT applications using incremental precision and multi-Level classification

Abstract

This paper presents a novel incremental-precision classification approach that leads to a reduction in energy consumption of linear classifiers for IoT applications. Features are first input to a low-precision classifier. If the classifier successfully classifies the sample, then the process terminates. Otherwise, the classification performance is incrementally improved by using a classifier of higher precision. This process is repeated until the classification is complete. The argument is that many samples can be classified using the low-precision classifier, leading to a reduction in energy. To achieve incremental-precision, a novel data-path decomposition is proposed to design of fixed-width adders and multipliers. These components improve the precision without recalculating the outputs, thus reducing energy. Using a linear classification example, it is shown that the proposed incremental-precision based multi-level classifier approach can reduce energy by about 41% while achieving comparable accuracies as that of a full-precision system.