Deep Learning FPGA Deployment on Xilinx ZCU102
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I would like to Run a Deep Learning Networks on Xilinx ZCU102 board, as shown here: Run a Deep Learning Network on FPGA with Live Camera Input - MATLAB & Simulink (mathworks.com)
But I can't find the model "Deep Learning Pre-Process Hardware Algorithm Target Model" mentioned in the link.
Would it be possible to get an off-line model ?
My matlab version is R2021a.
Thanks in advance.
5 Comments
Steve Kuznicki
on 16 Mar 2022
Janny, You will need to provide me with your email address for me to send this model and support package (for R2021aUpdate4).
giving love
on 23 May 2024
I would like to Run a Deep Learning Networks on Xilinx ZCU102 board, as shown here: Run a Deep Learning Network on FPGA with Live Camera Input - MATLAB & Simulink (mathworks.com)
But I also can't find the model "Deep Learning Pre-Process Hardware Algorithm Target Model" mentioned in the link.
my email is as follows:
Thank you!
Answers (2)
Kamil Sawicki
on 7 Jun 2022
Hello,
@janny ring could you please share some of your experience with me regarding example model that you mentioned. Right now I possess zcu102 and wonder if this is right direction before I purchase FMC HDMI sister card. Did you have to make any aditional purchases, what camera did you use and did you manage to use other neural networks with this example?
Thank you for your help in advance.
3 Comments
Steve Kuznicki
on 7 Jun 2022
Hi Kamil,
A regular 1080p HDMI "source" and "monitor" will do just fine here. I've used a GoPro and Sony Camera and even an IPad or Laptop to provide the HDMI Input. Currently I use a AIDA HDMI camera. The Monitor can be any with an HDMI input although some odd/cheaper ones may not work but I rarely run into that.
Different networks can surely work with this example. The "Pre-Processing" used here is really implementing the ImageInputLayer (zscore or zerocenter normalization) so your network will have to have the same layer, otherwise you can modify the Simulink model and regenerate the bitstream. The Post-processing is done on the ARM Processor and implements the layers the DLP do not execute. You can see which layers are Hardware vs Software (i.e. not executed in the DLP) when you "compile" the network using the Workflow object. So the Resnet18 and the Yolov2Resnet18 networks have different post-processing and therefore 2 different Simulink models are provided.
Kamil Sawicki
on 8 Jun 2022
Thank you for quick answer,
Cameras with HDMI output are not that common, but I understand why this type of input was chose over USB for example, but probably for now I could just stick with PC as a data source. Could you please tell me what tools are required to edit this example, can everything be done with Simulink and Matlab scripts, or would I have to use Vivado in order to get it going with my custom neural network?
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