Trainable and Low-Cost SMO Pattern Classifier Implemented via MCMC and SFBS Technologies

Peng, Chih-Hsiang; Kuan, Ta-Wen; Lin, Po-Chuan; Wang, Jhing-Fa; Wu, Guo-Ji

doi:10.1109/tvlsi.2014.2362150

Cited by 8 publications

(1 citation statement)

References 27 publications

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“…This method provides guidance for online training of SVM. For the key steps of SMO to satisfy Karush-Kuhn-Tucher (KKT) conditions during training, Jhing-Fa Wang et al studied the implementation of SMO, which includes Intellectual Property (IP) core implementation of SMO [16], an efficient configurable chip design based on SMO acceleration training method [17], and implementation of a low cost trainable SMO mode classifier [18]. The above research adopts a development method close to the underlying layer.…”

Section: Introductionmentioning

confidence: 99%

Research and Implementation of ε-SVR Training Method Based on FPGA

Liu

et al. 2019

Electronics

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Online training of Support Vector Regression (SVR) in the field of machine learning is a computationally complex algorithm. Due to the need for multiple iterative processing in training, SVR training is usually implemented on computer, and the existing training methods cannot be directly implemented on Field-Programmable Gate Array (FPGA), which restricts the application range. This paper reconstructs the training framework and implementation without precision loss to reduce the total latency required for matrix update, reducing time consumption by 90%. A general ε-SVR training system with low latency is implemented on Zynq platform. Taking the regression of samples in two-dimensional as an example, the maximum acceleration ratio is 27.014× compared with microcontroller platform and the energy consumption is 12.449% of microcontroller. From the experiments for the University of California, Riverside (UCR) time series data set. The regression results obtain excellent regression effects. The minimum coefficient of determination is 0.996, and running time is less than 30 ms, which can meet the requirements of different applications for real-time regression.

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Section: Introductionmentioning

confidence: 99%