Get Started with Deep Learning FPGA Deployment on Xilinx ZC706 SoC

This example uses:

Deep Learning HDL Toolbox Deep Learning HDL Toolbox
Deep Learning HDL Toolbox Support Package for Xilinx FPGA and SoC Devices Deep Learning HDL Toolbox Support Package for Xilinx FPGA and SoC Devices
Deep Learning Toolbox Deep Learning Toolbox

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.

Prerequisites

Xilinx® Zynq® ZC706 Evaluation Kit
Deep Learning HDL Toolbox™ Support Package for Xilinx® FPGA and SoC
Deep Learning Toolbox™
Deep Learning HDL Toolbox™

Load Pretrained Series Network

Load the pretrained series network trained on the Modified National Institute of Standards and Technology (MNIST) database.

snet = getDigitsNetwork;

View the layers of the pretrained series network, by using the analyzeNetwork function.

analyzeNetwork(snet)

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 2020.2. To set the Xilinx Vivado toolpath, enter:

% hdlsetuptoolpath('ToolName', 'Xilinx Vivado', 'ToolPath', 'C:\Xilinx\Vivado\2020.2\bin\vivado.bat');

hTarget = dlhdl.Target('Xilinx');

Create WorkFlow Object

Create an object of the dlhdl.Workflow class. 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

Run the compile function of the dlhdl.Workflow object, to compile the MNIST series network.

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 and displays progress messages and the time it takes to deploy the network.

hW.deploy

### Programming FPGA Bitstream using JTAG...
### Programming the FPGA bitstream has been completed successfully.
### Loading weights to FC Processor.
### FC Weights loaded. Current time is 12-Jun-2020 14:54:22

Load Example Image

Load the example image.

inputImg = imread('five_28x28.pgm');
imshow(inputImg);

Run Prediction

Execute the predict function of the dlhdl.Workflow object and display the prediction result and network performance.

[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                      80141                  0.00160                       1              80182            623.6
    conv_module              47601                  0.00095 
        conv_1               10047                  0.00020 
        maxpool_1             6999                  0.00014 
        conv_2               11367                  0.00023 
        maxpool_2             5465                  0.00011 
        conv_3               13783                  0.00028 
    fc_module                32540                  0.00065 
        fc                   32540                  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