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If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Then these images will be loaded into next module - Face Detection, which is actually a deep learning application. For this module, its computation-intensive part convolutional operation will be loaded into DPU a soft core implemented in FPGA, programmable logic.
The face detection module produces the coordinates of faces in an image, using which ARM will abstract face images.
Having face images, we can make scores. In other words, these faces will be put into next module - Face Score, which is substantially a image classification CNN.
After scoring, we convert these images to video. The face detection module is githubed from Xilinx repository. The face score module is trained and converted to DPU-usable version by ourselves. We introduce our optimization steps below:. Due to the unclear doc provided, we didn't complete it. I'd like to thank all staffs and teachers, particularly Wei Liu and Jiahua Lu. However, from my own perspective, the price, develop efficiency, and flexibility can still block the way for FPGA to general computing.
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Get Started with AI Inference
Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. No description, website, or topics provided. Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit….Next, we'll show the steps to update the PetaLinux project to include the necessary libraries and drivers to create a Vitis software platform that's capable of supporting HW accelerated workflows, including a DPU-based Machine Learning application.
Once we have the hardware and software platform components completed, we'll use the Vitis development kit to combine them into a Vitis acceleration platform that we can then build hardware-accelerated software application against.
Clone this repository to your local machine, and download the reference files directory. After downloading the reference files, unzip them into the reference files directory in the cloned repository. The rest of the folders in this hierarchy will be blank after downloading and will be populated throughout the tutorial.
After this step, you will have bootable hardware and software images to launch a pipeline to view the input MIPI video from the Ultra We'll now make the necessary additions and modifications to the hardware design to prepare the design for software defined acceleration: open the base Vivado project to get started. As we add additional components to the hardware design to accomodate acceleration, we need to customize the Processing Subsystem.
Here, we will modify the configuration to create additional clocks, open up additional interrupt ports, and create the AXI master port so that we can add additional peripherals to the design. This portion of the process also allows us to "name" the port, giving it a shorter nickname to designate connections later on.
Similarly to how we designated the interfaces for the platform, we now have to indicate to the tools which clocks it should use for the accelerators that it places in the platform. In this case, the DPU uses two clocks a 1x and a 2x clock so we will indicate to the platform both a and MHz clock. The DPU can be clocked faster or slower than this rate, and this rate was chosen to balance power and framerate performance in our application.
In this design, we've chosen to place the original components the MIPI subsystem on a seperate clock coming from the PS. This allows us to make sure that any changes in clock frequency or clock gating to the original or acceleration components will not affect the operation of the other.
The default scheduling mode for the acceleration kernels is polled. In order to enable interrupt based processing within our platform, we need to add an interrupt controller. Within the current design, we will connect up a constant "gnd" to the interrupt controller and not connecting any valid interrupt sources at this time.
Note here: we're not going to build this project to a bitstream. The Vitis tool will utilize this archive to import the design, compose in our hardware accelerators, and at that point, it will build a bitstream. We'll automate a portion of this process using the dsa. The software platform requires some changes to be made to the Petalinux project, adding the necessary Xilinx Runtime XRT components into the design.
The first step to creating our acceleration platform is adding in the library components mentioned above: the Xilinx Runtime, and the DPU runtime dnndk.
These come in the form of recipes, which we'll add into the user layer within our Petalinux build. First, we'll copy over the files and build recipes, and then we will enable them through the Petalinux root filesystem configuration menu. The Linux Device Tree needs to be modified so that the Xilinx Runtime kernel drivers are probed correctly.
Now that we've made all of the necessary configuration changes for the Petalinux build, we can kick off the build. This may take quite a while, or a short time given the processing power on your machine. After the Linux build is complete, we need to move all of the built software components into a common directory.
By placing all of our boot components in one directory, it makes it easy when packaging up the Hardware and Software sides into the resulting platform. We will also use Petalinux to build the sysroot in order to provide the complete cross-compilation environment for this software platform.
Now that we have built the hardware XSA and software Linux image and boot elf files components for the platform, we can use these components to generate and export our custom user-defined platform. We're going to walk through these steps in the Xilinx Vitis development kit. Now that the platform has been generated, you'll note that there is an "export" directory. This export directory is the complete, generated platform and can be zipped up and shared - providing the components to enable new developers on the custom platform.
We will use the pre-generated Xilinx Deep Learning Processor DPU as our acceleration kernel and compile this kernel into the platform using the Xilinx Vitis IDE, and then build a user-space application calling that hardware to run our custom Face Detection application.
We'll start by creating the new application project. In the Vitis tool, the Application Project exists inside of a System Project container in order to provide a method for cohesive system development across the enabled domains in the platform for instance, A53 and R5. Since we're working in the same workspace as before, we can simply target the platform that we generated earlier - but you can also add additional platform repos by clicking the "plus" button and pointing to the directory which contains your xpfm within the Platform Selection dialog.I have built the block design in Vivado and exported the.
Now I have built a Petalinux project with the template flow and found a cached version of a DPU integration tutorial from Xilinx, where I'm stuck on the following points:. Is this how you're supposed to do it? This is needed due to the OpenCV v3. This is needed to make the pre-built dpu kernel module dpu. However, I could only find those files specifically for the supported reference boards. However, I don't know how to add the source for this package to Petalinux to find this package.
So any help with any of these things would be very much appreciated. Or maybe an update can be given as to when the updated DPU Integration tutorial will go online again. I have zero experience with Petalinux, so coming up with a solution to any of these is quite challenging and google isn't helping that much.
View solution in original post. The udpated tutorial and all of the files needed to complete the flow should be avaiable in the next week. I would recommend waiting until it is avaiable so you can see the implementation details of all of the steps mentioned below. Thanks terryo. Well, it looks like a section of the tutorial went missing when it was published.
There is an entire sectino on building the applications in Xilinx SDK. I have someone looking into it, hope to have it fixed today. Does it possible to use DPU with Zynq not ultrascale? Can I go through this guide step by step and get it running on Zynq based video processing board? How difficult to do this?
Sign In Help. Turn on suggestions. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.It is useful as a base platform for exercising Vitis capabilities and topologies on the zcu board.
The platform build process is entirely scripted. Note that as this platform build process involves cross-compiling Linux, build of the platform is supported on Linux environments only although it is possible to build inside a VM or Docker container.
Also note that the default PetaLinux configuration uses local scratchpad areas. This will not work if you are building on a networked file system; Yocto will error out.
DPUv3E for Alveo Accelerator Card with HBM
After cloning the platform source, and with both Vivado and PetaLinux set up, run make from the top-level platform directory. A bundled Yocto SDK "sysroot" is not available with this package by default. To build non-trivial Linux software for this platform sysroot need to be built and installed.
To cross-compile against this platform from the command line, source the environment-setup-aarchxilinx-linux script to set up your environment cross compiler, build tools, libraries, etc. This packages comes with sources to generate hardware specification file xsa from Vivado, petlainux sources to generate the image. This platform is built with To build the platforms with latest Skip to content. Branch: master. Create new file Find file History.
Latest commit. Latest commit Feb 28, Build instructions This packages comes with sources to generate hardware specification file xsa from Vivado, petlainux sources to generate the image. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Adding missing figures. Feb 28, Removal of redundant files. Feb 17, Feb 14, The degree of parallelism utilized in the engine is a design parameter and application. Supports configurable AXI master interface with 64 or bits for accessing data depending on the target device.
The detailed hardware architecture of the DPU is shown in the following figure. After start-up, the DPU fetches instructions from off-chip memory to control the operation of the computing engine. The instructions are generated by the DNNC where substantial optimizations have been performed. On-chip memory is used to buffer input, intermediate, and output data to achieve high throughput and efficiency.
The data is reused as much as possible to reduce the memory bandwidth. A deep pipelined design is used for the computing engine. The processing elements PE take full advantage of the finegrained building blocks such as multipliers, adders and accumulators in Xilinx devices.
There are three dimensions of parallelism in the DPU convolution architecture - pixel parallelism, input channel parallelism, and output channel parallelism. The input channel parallelism is always equal to the output channel parallelism.
The different architectures require different programmable logic resources. The larger architectures can achieve higher performance with more resources. The parallelism for the different architectures is listed in the table. Skip to content. Branch: master. Create new file Find file History. Latest commit. Latest commit f87d Apr 15, Features One AXI salve interface for accessing configuration and status registers. One AXI master interface for accessing instructions.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.
This tutorial demonstrates how to build a custom system that utilizes the 1. So we suggest use at least 16G SD card and format it with two partitions. You should end up with a directory structure as shown in the following figure:.
TIP: There is a file called commands. Copy and paste the file from this location to save time.
They must be installed separately. It is assumed that this step is already completed. Note: You should see a message indicating that one repository and one IP is added. To use the pre-built option, execute the following command to copy the pre-built. Note: To save time, we can skip building the Vivado project during this lab session and manually export a pre-built.
When the bitstream generation process is complete, do the following steps to export the.How to Create Linux Applications using Xilinx SDK
You can begin with the PetaLinux flow, once the hardware definition file. At this point, you should have exported the.
Tip: To speed up text entry, use commands. It is highly recommended that you copy and paste the commands to avoid command-line errors. Note: All the commands are not available in the commands. Make sure you see this lab document for proper sequencing. Use the following command to create a new PetaLinux project based on the Zynq template in a new directory named petalinux.
In this step, you will add or edit some Yocto recipes to customize the kernel and rootfs and add the dnndk files. At this time, the DPU is not supported by the device-tree generator. Therefore, we need to manually add a device-tree node to the DPU, based on our hardware settings. Once the board has booted, login using the following credentials:.
Advance using MobaXterm which auto-enable X11 forward in default. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Verilog Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.
Latest commit Fetching latest commit…. Generate the Linux platform in PetaLinux. Use a. Examine the DPU configuration and connections.We also have published an article on it and showed up the results there.
View solution in original post. I have attached a script to create the project in Place it in the pl dir and execute vivado -source zcu You can use the existing zcudpu-trd-v There is one change you have to make:. First run the script. Then run the zcu script, this will create a new project for the zcu The timing results post Synthesis are using estimated delays for routing based on wire length, and there will be differnces from the final timing post route. A large negative WNS after Sythesis means the design is very unlikely to meet timing and you need to change the design or Sythesis settings.
I recommend staying with this version of Vivado, until the next version of the DPU comes. Thanks for your instruction! Which is use ZU11EG. I'm set up with the zcu board and have downloaded the image for the zcu to the SD card and run it.
I have also downloaded the zcu directory of tools for the Dnndk to the board, and have run some of the demos. Running Ubuntu I should therefore have all of the tools that I need. As others in this thread have tried, I desire to run some of the AI demos which were intended for the zcu I downloaded and tried the " vivado -source zcu Also download the zcu After the project created for ZCU, goto Tcl Console window of that zcu project and do "source zcu I hope this helps you!