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Get Your Xilinx FPGA/Programmable SoC Questions Answered here

rscasny

In the past year or so, the element14 community has been offering quite a few programs, contests, and initatives around Xilinx's FPGA and heterogeneous SoC, ZYNQ. We have hosted webinars, run roadtests, and offered a training program last year called Path to Programmable.

 

I see element14 member interest in Xilinx product knowledge on the rise. I plan on offering more Xilinx-related projects and roadtests in the coming months. (Stay tuned to Path to Programmable 2 with the Ultra96v2) Given all this activity, I thought it would be a great idea to bring in a Xilinx product expert for some well needed Q&A time. So let me introduce you to Adam Taylor ( ).

 

I believe Adam has been an element14 member for several years. He is the Director of ADIUVO Engineering. He is a Chartered Engineer and Fellow of the Institute of Engineering and Technology. He is well known for his Microzed Chronicles. He writes the Exploring the Programmable World for element14. Adam has been instrumental in developing element14's FPGA/Programmable SoC Essentials.

 

Adam also is an expert in the PYNQ framework: Python for ZYNQ productivity. So, if you are asoftware developer who wants to explore the Programmable world, I'd encourage you to ask Adam your top questions.

 

So, if you have any questions revolving around FPGAs, programmable SoCs, a project in progress, perhaps even a question about Vivado, please click REPLY and asked them here.

 

Sincerely,

 

Randall Scasny

-element14 Team

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  • A couple of areas I'm interested in exploring with FPGA ( initially using the TUL PYNQ-Z2 board ) are:

     

    1) HDMI video capture and processing where the PYNQ-Z2 is used for low cost HDMI capture / pass through for basic object/pedestrian detection/tracking which is then further used for indexing video recordings or automated tracking such as remote camera control. 
    (It would also be interesting to look at accessing the HDMI I2C bus to extract/inject EDID information via the PYNQ framework to enable video signal diagnostic type applications.)

     

    Some interesting online resources I've found in this area so far are:

     

     

     

     

    Some inital questions perhaps arise around what are the quick wins in terms of hardware acceleration vs productivity in this area. A lot of the processing appears to still be done in software on the ARM processor as oppossed to being accelerated on the FPGA. Are there perhaps more appropriate computer vision libraries that would make better use of the FPGA hardware ?

     

    2) Serial data capture and processing where the FPGA hardware or MicroBlaze subsystem are used to create a customizable UART to decode the incoming serial data and make it available via the PYNQ framework. Perhaps using the hardware to detect
    certain conditions to filter the data stream off-loading some of the processing.


    One application I had in mind was to be able to ingest a DMX512 lighting protocol on a RS-485 interface and have Python scripts react when certain data frames are sent out from a lighting console. Perhaps using the HDMI inputs and outputs to pass through live video and switch it with still images (or computer generated art / real time data from the web) controlled by the lighting console to create intelligent lighting displays using video projection or video walls.

     

    For experimentation purposes one perhaps could even use the MicroBlaze subsystem to set up a virtual DMX512 transmitter and and virtual DMX512 receiver and access  both via the PYNQ framework all on the one board without any worries of timing issues.

     

    However at a more general level, similar approaches perhaps could be used for more general serial data acquisition and status display on an attached HDMI screen as a protocol diagnostic tool.

     

    Unfortunately there doesn't yet appear to be PYNQ driver available for the existing PMOD 485 interface
    https://store.digilentinc.com/pmod-rs485-high-speed-isolated-communication/
    and I've not found much in the way of associated on-line FPGA tutorials in this particular area, especially not with using the PYNQ framework.

  • in reply to beacon_dave

    The Pynq Z2 should give a similar performance if you use the OpenCV overlay as outlined in the blog of mine you mention. If you want to look at the Ultra96 which is the best board available for many projects I think in my opinion then you can get significant speeds up including >100 frames per second for optical flow algorithm implementation https://blog.hackster.io/microzed-chronicles-ultra96-and-pynq-da3b22cc982

     

    Regarding the PYNQ driver for the Pmod485 it is possible to write C code for the micro-blaze in the Jupyter note book, so you could use this approach and leverage the Pmod485 driver from the Digilent PMod library. I covered it briefly in a blog here https://blog.hackster.io/pynq-edition-interfacing-with-pmods-arduino-and-raspberry-pi-61676f9f0fab

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