VLSI Design of an SVM Learning Core on Sequential Minimal Optimization Algorithm
Ta-Wen Kuan, Jhing-Fa Wang, Jia‐Ching Wang, Po‐Chuan Lin, Gaung-Hui Gu · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2011
The sequential minimal optimization (SMO) algorithm has been extensively employed to train the support vector machine (SVM). This work presents an efficient application specific integrated circuit chip design for sequential minimal optimization. This chip is implemented as an intellectual property core, suitable for use in an SVM-based recognition system on a chip. The proposed SMO chip was tested and found to be fully functional, using a prototype system based on the Altera DE2 board with a Cyclone II 2C70 field-programmable gate array.