Name
Abstract | ||
|---|---|---|
We present here a hardware-friendly version of the Support Vector Machine (SVM), which is useful to implement its feed-forward phase on limited-resources devices such as Field Programmable Gate Arrays (FPGAs) or microcontrollers, where a floating-point unit is seldom available. Our proposal is tested on a machine-vision benchmark dataset for automotive applications. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IJCNN.2007.4371156 | 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 |
Keywords | Field | DocType |
support vector machine,machine vision,field programmable gate array,support vector machines,embedded systems,feed forward,floating point arithmetic,floating point unit | Automotive electronics,Floating-point unit,Computer science,Floating point,Support vector machine,Field-programmable gate array,Microcontroller,Computer hardware,Embedded system,Automotive industry | Conference |
ISSN | Citations | PageRank |
1098-7576 | 20 | 1.38 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Anguita | 1 | 1001 | 70.58 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Stefano Pischiutta | 3 | 65 | 4.12 |
| Sandro Ridella | 4 | 677 | 140.62 |