FPGASVM: A Framework for Accelerating Kernelized Support Vector Machine

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Mudhar Bin Rabieah, Christos-Savvas Bouganis ;
Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications at KDD 2016, PMLR 53:68-84, 2016.

Abstract

Support Vector Machines (SVM) are powerful supervised learnings method in machine learning. However, their applicability to large problems, where frequent retraining of the system is required, 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 kernelized SVM training using ensemble learning is presented. The proposed framework builds on the FPGA architecture and utilises a cascaded multiprecision 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, providing a stepping stone for researchers and practitioners to tackle large-scale SVM problems that require frequent retraining.

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