Performance Evaluation of gcForest inferencing on multi-core CPU and FPGA

Abstract

Decision forests have proved to be useful in machine learning tasks. gcForest is a model that leverages ensembles of decision forests for classification. It combines several decision forests and by adding properties and layered architecture in such a way that it has been proven to give competitive results compared to convolutional neural networks. This paper analyzes the performance of a gcForest model trained on the MNIST digit classification data set on a multi-core CPU based system. Using a performance model-based approach it also presents an analysis of performance on a well-endowed FPGA accelerator card for the same model. It is concluded that the multi-core CPU system can deliver more throughput than the FPGA with batched workload, while the FPGA offers lower latency for a single inference. We also analyze the scalability of the gcForest model on the multi-core server system and with the help of experiments and models, uncover ways to improve the scalability.