Name
Title | ||
|---|---|---|
Implementing a SoC-FPGA Based Acceleration System for On-Board SVM Training for Robotic Transtibial Prostheses |
Abstract | ||
|---|---|---|
This paper presents an acceleration system for on-board support vector machine (SVM) model training for robotic transtibial prosthesis based on system on chip with field-programmable gate array (SoC-FPGA) hardware. A hardware prototype was developed and SVM-based model training algorithm was implemented with high-level synthesis technology. Experiments on a transtibial amputee subject demonstrated that the proposed system provided good speedups over ARM-based implementation for on-board training in six locomotion identification tasks (standing, level-ground walking, ramp ascent, ramp descent, stair ascent, stair descent). Meanwhile, the additional power consumption was not significant and acceptable. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/RCAR.2018.8621732 | 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR) |
Keywords | Field | DocType |
SoC-FPGA based acceleration system,On-Board SVM Training,robotic transtibial prostheses,on-board support vector machine model training,robotic transtibial prosthesis,field-programmable gate array hardware,hardware prototype,SVM-based model training algorithm,high-level synthesis technology,on-board training,ARM,power consumption | System on a chip,Simulation,Computer science,Support vector machine,Transtibial prosthesis,Field-programmable gate array,Gate array,Acceleration,Power consumption | Conference |
ISBN | Citations | PageRank |
978-1-5386-6870-2 | 0 | 0.34 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingeng Mai | 1 | 4 | 8.76 |
| Dongfang Xu | 2 | 1 | 3.79 |
| Hao-lin Li | 3 | 28 | 5.44 |
| Shichang Zhang | 4 | 0 | 0.68 |
| Jiaying Tan | 5 | 0 | 0.68 |
| Qining Wang | 6 | 167 | 39.64 |