FPGA based nonlinear Support Vector Machine training using an ensemble learning

Abstract

Support Vector Machines (SVMs) are powerful supervised learning methods in machine learning. However, their applicability to large problems has been limited due to the time consuming training stage whose computational cost scales quadratically with the number of examples. In this work, a complete FPGA-based system for nonlinear SVM training using ensemble learning is presented. The proposed framework builds on the FPGA architecture and utilizes a cascaded multi-precision training flow, exploits the heterogeneity within the training problem by tuning the number representation used, and supports ensemble training tuned to each internal memory structure so to address very large datasets. Its performance evaluation shows that the proposed system achieves more than an order of magnitude better results compared to state-of-the-art CPU and GPU-based implementations.