| Issue |
E3S Web Conf.
Volume 229, 2021
The 3rd International Conference of Computer Science and Renewable Energies (ICCSRE’2020)
|
|
|---|---|---|
| Article Number | 01024 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/e3sconf/202122901024 | |
| Published online | 25 January 2021 | |
FPGA-based Hardware Acceleration for SVM Machine Learning Algorithm
1
Laboratory of Energy Engineering, Materials and Systems, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco
2
National School of Applied Sciences, Cady Ayad University, Marrakech, Morocco
* Corresponding author: jfatimzehra@gmail.com
object recognition algorithms are both large consumers of computing power and memory which affects the quality and performance especially when it comes to large image datasets, in this paper we propose an algorithm for fruit/plant recognition that we will accelerate it using the PYNQ Board to evaluate the execution time and the accuracy of the classifier.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.