Implementation and Scalability Evaluation of Random Forests for In-Switch Inference

Abstract

We comparatively evaluate state-of-the-art solutions for in-switch machine learning inference. We demonstrate that random forest (RF) models attain accuracies on par with those of approaches based on neural networks, which are also less amenable to in-switch operation. Next, we implement the top two solutions for in-switch random forest representation using a unified framework that we propose to ensure their fair comparison. We then verify their performance, resource consumption and scalability with respect to the capabilities of production-grade programmable switches.