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Get Started with Deep Learning FPGA Deployment on Xilinx ZC706 SoC

This example shows how to create, compile, and deploy a dlhdl.Workflow object that has a handwritten character detection series network as the network object using the Deep Learning HDL Toolbox™ Support Package for Xilinx® FPGA and SoC. Use MATLAB® to retrieve the prediction results from the target device.


  • Xilinx Zynq ZC706 Evaluation Kit

  • Deep Learning HDL Toolbox™ Support Package for Xilinx FPGA and SoC

  • Deep Learning Toolbox™

  • Deep Learning HDL Toolbox™

Load the Pretrained Series Network

To load the pretrained series network, MNIST, enter these commands:

snet = getDigitsNetwork;

To view the layers of the pretrained series network, enter:


Create Target Object

Create a target object with a custom name for your target device and an interface to connect your target device to the host computer. Interface options are JTAG and Ethernet. To use JTAG, install Xilinx Vivado® Design Suite 2019.2. To set the Xilinx Vivado toolpath, enter:

% hdlsetuptoolpath('ToolName', 'Xilinx Vivado', 'ToolPath', 'C:\Xilinx\Vivado\2019.2\bin\vivado.bat');
hTarget = dlhdl.Target('Xilinx');

Create Workflow Object

Create an object of the dlhdl.Workflow class. When you create the object, specify the network and the bitstream name. Specify the saved pretrained MNIST neural network, snet, as the network. Make sure that the bitstream name matches the data type and the FPGA board that you are targeting. In this example the target FPGA board is the Xilinx Zynq ZC706 board. The bitstream uses a single data type.

hW = dlhdl.Workflow('Network', snet, 'Bitstream', 'zc706_single','Target',hTarget);

Compile MNIST Series Network

To compile the MNIST series network, run the compile function of the dlhdl.Workflow object.


dn = hW.compile
### Optimizing series network: Fused 'nnet.cnn.layer.BatchNormalizationLayer' into 'nnet.cnn.layer.Convolution2DLayer'
          offset_name          offset_address    allocated_space 
    _______________________    ______________    ________________

    "InputDataOffset"           "0x00000000"     "4.0 MB"        
    "OutputResultOffset"        "0x00400000"     "4.0 MB"        
    "SystemBufferOffset"        "0x00800000"     "28.0 MB"       
    "InstructionDataOffset"     "0x02400000"     "4.0 MB"        
    "ConvWeightDataOffset"      "0x02800000"     "4.0 MB"        
    "FCWeightDataOffset"        "0x02c00000"     "4.0 MB"        
    "EndOffset"                 "0x03000000"     "Total: 48.0 MB"
dn = struct with fields:
       Operators: [1×1 struct]
    LayerConfigs: [1×1 struct]
      NetConfigs: [1×1 struct]

Program Bitstream onto FPGA and Download Network Weights

To deploy the network on the Xilinx ZC706 hardware, run the deploy function of the dlhdl.Workflow object. This function uses the output of the compile function to program the FPGA board by using the programming file. It also downloads the network weights and biases. The deploy function starts programming the FPGA device, displays progress messages, and the time it takes to deploy the network.

### Programming FPGA Bitstream using JTAG...
### Programming the FPGA bitstream has been completed successfully.
### Loading weights to FC Processor.
### FC Weights loaded. Current time is 28-Jun-2020 13:23:02

Load Example Image

Load the example image.

inputImg = imread('five_28x28.pgm');

Run Prediction

Execute the predict function of the dlhdl.Workflow object, and then display the FPGA result.

[prediction, speed] = hW.predict(single(inputImg),'Profile','on');
### Finished writing input activations.
### Running single input activations.

              Deep Learning Processor Profiler Performance Results

                   LastLayerLatency(cycles)   LastLayerLatency(seconds)       FramesNum      Total Latency     Frames/s
                         -------------             -------------              ---------        ---------       ---------
Network                      80058                  0.00160                       1              80100            624.2
    conv_module              47525                  0.00095 
        conv_1               10025                  0.00020 
        maxpool_1             6999                  0.00014 
        conv_2               11361                  0.00023 
        maxpool_2             5459                  0.00011 
        conv_3               13741                  0.00027 
    fc_module                32533                  0.00065 
        fc                   32533                  0.00065 
 * The clock frequency of the DL processor is: 50MHz
[val, idx] = max(prediction);
fprintf('The prediction result is %d\n', idx-1);
The prediction result is 5