On Feasibility of Decision Trees for Edge Intelligence in Highly Constrained Internet-of-Things (IoT)

Internet-of-Things (IoT) edge devices have limited resources in terms of area and power. Machine Learning based intelligent filtering can be effective in reducing the data footprint. In this work, we report a feasibility study of using decision trees (DTs) on the edge. The main contribution of this work is to demonstrate that decision trees are equally effective compared to popular neural networks (multi-layer perceptrons). We trained four datasets (Iris, Heart Disease, Breast Cancer, and Credit Card) with supervised decision tree-based learning with accuracy comparable to that of MLPs. We synthesized the DTs to gate-level implementation with the Synopsys Design Compiler in 32 nm CMOS technology node. Compared to MLP implementations, DTs can save approximately 97-98% in both area and power.