Shakan: Training-Inference co-design for Oblique Random Forests on Embedded Devices

Embedded systems are increasingly leveraging Artificial Intelligence of Things (AIoT) to enable real-time decision-making in critical applications, such as autonomous navigation and medical diagnostics. In these contexts, Random Forests (RFs) have been widely adopted due to their inherent parallelism. However, RFs rely on axis-aligned splits, which limit their ability to model complex decision boundaries. Oblique Random Forests (ORFs), which employ hyperplane-based splits, offer a more expressive alternative by improving classification accuracy. Despite their advantages, inference of ORFs is resource-consuming, prohibiting the implementation of such models on resource-constrained hardware devices.In this work, we present Shakan, a novel framework for Oblique Decision Trees (ODTs) inference on embedded systems. We introduce a new training technique designed to mitigate both training complexity and overfitting while enabling low-latency inference in hardware, along with a new architecture that maximizes performance and optimizes resource usage. Shakan enables, on resource-constrained devices, the inference of several ORFs configurations that can provide either a significant increase in accuracy or a notable speedup in terms of inference latency compared to state-of-the-art accelerators for traditional RFs on embedded devices. The most accurate configurations provide average accuracy improvements above 5% with similar latency, while the fastest configurations achieve speedups of 1140×, 214×, and 29× for tree depths of 5, 7, and 9, respectively, with comparable accuracy.