Implementing Hardware Decision Tree Prediction: A Scalable Approach
Mario Barbareschi · 2016
Performance of data classification systems is one of the most important aspect when involved data volume, combined with classical computing approaches, does not match tight constraints on latency and throughput. Indeed, even though the classification accuracy of modern machine learning tools is very suitable for the adoption in many applications, they require many computational resources and elaboration time. In the literature, a huge effort has been done to define new architectures and several hardware implementations have been introduced. In this paper, we show a hardware implementation for the classification system based on the Decision Tree and we formally give a demonstration of its scalability in terms of required resources. At the end, with a significant amount of experimental evidences, we prove that the occupied area and power consumption have a linear behavior against the classification parameters.