Approximation Workflow for Energy-Efficient Comparators in Decision Tree Applications

The increasing use of Machine Learning applications has caused a high demand for design techniques targeting the trade-off between energy consumption and accuracy in inference models. In this scenario, Decision Trees are models widely used in embedded systems and Internet of Things applications. They are less resource intensive than Neural Networks, and maintain acceptable accuracy results for various problems. This project proposes a framework for evaluating the improvement of the power-accuracy trade-off in decision tree models using approximate circuits. The results of the electrical evaluation and accuracy provided in each step are obtained by applying different approximation and quantization techniques. This information can be used together with power-accuracy metrics to find the best comparator circuits for different applications of Decision Trees and dedicated hardware requirements.