Exploiting Modular Redundancy for approximating Random Forest classifiers

• A modular redundancy-based approximation is proposed for decision tree ensembles. • Modular redundancy is used to select only a subset of trees for classifying each class label. • This strategy allows aggressive approximation while preserving accuracy. • The effectiveness of the solution is demonstrated and shown. The deployment of machine learning models at the edge is crucial for enabling low-latency decision-making, optimizing resource utilization, and enhancing data confidentiality. Random Forest classifiers have proven to be highly accurate while offering computationally efficient inference, making them well-suited for resource-constrained edge devices. However, as the volume of training data grows, the complexity and size of these models also increase, limiting their deployment in edge computing scenarios. In order to address this challenge, we propose a novel approximation strategy for Random Forest classifiers leveraging on the concept of modular redundancy. In particular, our approach imposes that each target class is determined by only a subset of trees in a modular redundant fashion. This allows to prune from each tree the leaves related to no-longer relevant classes, significantly reducing the size of the model. To achieve an optimal balance between accuracy and resource savings with minimal computational time, we introduce an heuristic algorithm that determine the best subset of trees for each class. We evaluate our approach on multiple UCI machine learning datasets using a hardware accelerator for tree ensembles, demonstrating its effectiveness. The result shows that, on average, a 2.5% reduction in accuracy leads to save up to 50% in hardware overhead and energy consumption.