Adding DPU IP¶

The AMD Deep Learning Processor Unit (DPU) is a configurable computation engine dedicated to convolutional neural networks. The degree of parallelism utilized in the engine is a design parameter and application. It includes highly optimized instructions and supports most convolutional neural networks like VGG, ResNet, GoogleNet, YOLO, SSD, MobileNet, FPN, and others.

Vitis AI Reference Design¶

Vitis AI DPU IP and Reference Designs distribute the DPU IP and reference designs. This page has TRD designs and IP related to the Kria SOM target. The Vitis DPU TRD tutorial explains the following:

How to integrate a DPU in the Vitis and Vivado flow
How to change the configuration of the DPU
How to integrate the DPU in the customer platform in the Vitis 2022.1 environment

In this tutorial, you only look at the steps to integrate the DPU; for detailed information on the configuration, refer to Vitis DPU TRD.

In summary, the Vitis DPU TRD mentions that to add a DPU to the project, you must have the following files in the repo:

DPU IP: Found in the Kria repo in the top-level directory.
Vitis DPU integration files: Found in the Kria repo in the kernel-level directory.
kernel_xml: Vitis XML files can be found in the Kria repo in the kernel-level dir.
dpu_config.vh: DPU configuration file
scripts: Scripts to compile the DPU. These can be found in the Kria repo in the kernel-level dir.
prj_config: System architecture specification. These can be found in the Kria repo in the kernel-level dir.

IMPORTANT: In this step, you add the DPU kernel and modify the system configuration files.

Navigate to kv260/overlays/examples/smartcam, and replace the Makefile with the following Makefile, which includes the DPU IP and DPU Vitis files:

//make sure you are in the smartcam directory 

cp ../../../../../../../code_repo_kria_vitis_acceleration_flow_2022.1/ml_inference/vitis_platform_files/Makefile .

Navigate to kv260/overlays/examples/smartcam/prj_conf, and replace the “prj_config_1dpu” with the prj_config_1dpu which includes the DPU System configuration

cd prj_conf 
cp ../../../../../../code_repo_kria_vitis_acceleration_flow_2022.1/ml_inference/vitis_platform_files/prj_config_1dpu .

Configuration of the DPU¶

DPU configurations are mentioned in dpu_config.vh. This example uses a B3136 configuration.

DPU Configuration¶

The DPU configuration file is used to specify clocks, system connectivity, and the number of DPUs. In this design, you use the following specifications. If you do not specify the number of DPUs using the nk option, it defaults to one.

freqHz=300000000:DPUCZDX8G_1.aclk
freqHz=600000000:DPUCZDX8G_1.ap_clk_2
sp=DPUCZDX8G_1.M_AXI_GP0:HP1
sp=DPUCZDX8G_1.M_AXI_HP0:HP1
sp=DPUCZDX8G_1.M_AXI_HP2:HPC1

Next Steps¶

This completes the addition of the DPU IP. The next step is to compile and link the SmartCam hardware.

Compile and Link Smartcam Hardware¶

Run the following command to generate the binaries. The Makefile invokes Vivado to compile the DPU and resize the kernel. Once the compilation finishes, it links it with the platform to generate a xclbin.

cd ../../../
make clean
make overlay OVERLAY=smartcam

arch.json File¶

The arch.json file is an important file required by Vitis AI. Vitis generates the file during the compilation of the DPU. The Vitis-ai Model zoo has remade binaries for a fixed set of platforms. You can regenerate these models for the different targets using the JSON file. The Vitis AI compiler needs it to support model compilation with various DPU configurations. The arch.json file can be found in the prj/Vitis/binary_container_1/sd_card folder amd contains the following:

{ “fingerprint”:”0x101000016010406” }

Next Steps¶

This completes the DPU integration. The next step is to review the Overview of SmartCam VVAS plugins.

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