NDLI: FPGA based nonlinear Support Vector Machine training using an ensemble learning

Content Provider	IEEE Xplore Digital Library
Author	Bin Rabieah, M. Bouganis, C.-S.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK (Bin Rabieah, M.; Bouganis, C.-S.)
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.
Starting Page	1
Ending Page	4
File Size	364279
Page Count	4
File Format	PDF
ISBN	9780993428005
DOI	10.1109/FPL.2015.7293972
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-02
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Imperial College
Subject Keyword	Training Support vector machines Field programmable gate arrays Kernel Hardware Computer architecture Acceleration
Content Type	Text
Resource Type	Article

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