Fast and Energy-Efficient Oblique Decision Tree Implementation with Potential Error Detection

Abstract

In the contemporary landscape, with the proliferation of cyber-physical systems and the Internet of Things, intelligent embedded systems have become ubiquitous. These systems derive their intelligence from machine learning algorithms that are integrated within them. Among many machine learning algorithms, decision trees are often favored for implementation in such systems due to their simplicity and commendable classification performance. In this regard, we have proposed the efficient implementations of a fixed-point decision tree tailored for embedded systems. The proposed approach begins by identifying an input vector that might be classified differently by a fixed-point decision tree than by a floating-point decision tree. Upon identification, an error flag is activated, signaling a potential misclassification. This flag serves to bypass or disable the subsequent classification procedures for the identified input vector, thereby conserving energy and reducing classification latency. Subsequently, the input vector is alternatively classified based on class probabilities gathered during the training phase. In comparison with traditional fixed-point implementations, our proposed approach is proven to be 23.9% faster in terms of classification speed, consuming 11.5% less energy without compromising classification accuracy. The proposed implementation, if adopted in a smart embedded device, can provide a more responsive service to its users as well as longer battery life.