| Title | Hardware Design of Gaussian Kernel Function for Non-Linear SVM Classification |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wang, Yuanfa, Pang, Yu, Huang, Huan, Zhou, Qianneng, Luo, Jiasai |
| Conference Name | 2021 IEEE 14th International Conference on ASIC (ASICON) |
| Keywords | Classification algorithms, Conferences, digital computers, exponentiation, Hardware, Multiplexing, pubcrawl, resilience, Resiliency, Resource management, Scalability, Support vector machines |
| Abstract | High-performance implementation of non-linear support vector machine (SVM) function is important in many applications. This paper develops a hardware design of Gaussian kernel function with high-performance since it is one of the most modules in non-linear SVM. The designed Gaussian kernel function consists of Norm unit and exponentiation function unit. The Norm unit uses fewer subtractors and multiplexers. The exponentiation function unit performs modified coordinate rotation digital computer algorithm with wide range of convergence and high accuracy. The presented circuit is implemented on a Xilinx field-programmable gate array platform. The experimental results demonstrate that the designed circuit achieves low resource utilization and high efficiency with relative error 0.0001. |
| DOI | 10.1109/ASICON52560.2021.9620361 |
| Citation Key | wang_hardware_2021 |
Hardware Design of Gaussian Kernel Function for Non-Linear SVM Classification