A trained Convolutional Neural Network implemented on ZedBoard Zynq-7000 FPGA. Link to YouTube Video(s): https://www.youtube.com/watch?v=xoB--RFfy6I&feature=youtu.be
Project name: BeeBoard
Date: 30-Jul_2018
Version of uploaded archive: 1
University name: ISTANBUL TECHICAL UNIVERSITY
Supervisor name: Berna Ors Yalcin
Supervisor e-mail: [email protected]
Participant(s):
Ilayda Yaman https://www.linkedin.com/in/ilayda-yaman-9bba0ab1/
M. Tarik Tamyurek
Burak M. Gonultas https://www.linkedin.com/in/burak-mert-gonultas-94b045b1/
Email:
gonul004 [at] umn.edu
Board used: Digilent ZedBoard Zynq®-7000 ARM/FPGA SoC Development Board
Vivado Version: 2018.1
Brief description of project: A trained Convolutional Neural Network has been implemented on an FPGA evaluation board, ZedBoard Zynq-7000 FPGA, focused on fingerspelling recognition.
Description of archive (explain directory structure, documents and source files):
CNN folder includes Vivado files
MATLAB_Code folder includes files to verify the results obtained by the Vivado- Behavioral Synthesis
Instructions to build and test project
Step 1: Go to CNN folder for Vivado files of the project
Step 2: Run Behavioral Synthesis
Step 3: Obtain results for the hardware design
Step 4: Compare it with MATLAB results by running the "CNN.m" file inside the MATLAB_Code folder