high performance computing on graphics processing units: hgpu.org

Support Vector Machines (SVMs) are a powerful supervised learning tool, providing state-of-the-art accuracy at a cost of high computational complexity. The SVM classification suffers from linear dependencies on the number of the Support Vectors and the problem’s dimensionality. In this work, we propose a scalable FPGA architecture for the acceleration of SVM classification, which exploits the device heterogeneity and the dynamic range diversities among the dataset attributes. Furthermore, this work introduces the first FPGA-oriented cascade SVM classifier scheme, which intensifies the custom-arithmetic properties of the heterogeneous architecture and boosts the classification performance even more. The implementation results demonstrate the efficiency of the heterogeneous architecture, presenting a speed-up factor of 2-3 orders of magnitude, compared to the CPU implementation, while outperforming other proposed FPGA and GPU approaches by more than 7 times.