Implementing machine learning algorithms on FPGA for edge computing
In recent years, with the development of high-performance computing devices, convolutional neural network (CNN) has become one of the most popular machine learning algorithms. It has achieved unprecedented success in various fields of application. However, despite its great performance, traditional...
Saved in:
Main Author: | Chen, Zhuoran |
---|---|
Other Authors: | Lam Siew Kei |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/148052 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Hardware-assisted malware detection for embedded systems
by: Chua, Penelope Hui Eng
Published: (2022) -
Energy efficient circuits and architectural design for machine learning on edge
by: Chong, Yi Sheng
Published: (2023) -
FPGA acceleration of continual learning at the edge
by: Piyasena Gane Pathirannahelage Duvindu
Published: (2021) -
Intelligent high level synthesis for customization on reconfigurable platforms
by: Sharad Sinha
Published: (2014) -
Federated deep learning for edge computing (part I)
by: See, Ian Soong En
Published: (2020)