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 handwritten character detection series network to an FPGA and use MATLAB® to retrieve the prediction results.

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 Network

Load the pretrained series network trained on the Modified National Institute of Standards and Technology (MNIST) database[1].

snet = getDigitsNetwork;

View the layers of the pretrained series network by using the Deep network Designer app.

deepNetworkDesigner(snet)

Define FPGA Board Interface

Define the target FPGA board programming interface by using the dlhdl.Target object. Create a programming interface with custom name for your target device and a JTAG interface to connect the target device to the host computer. To use JTAG, install Xilinx™ Vivado™ Design Suite 2022.1. Set the toolpath by using the hdlsetuptoolpath function.

hdlsetuptoolpath('ToolName', 'Xilinx Vivado', 'ToolPath', 'C:\Xilinx\Vivado\2022.1\bin\vivado.bat');

hTarget = dlhdl.Target('Xilinx');

Prepare Network for Deployment

Prepare the network for deployment by creating a dlhdl.Workflow object. Specify the network and bitstream name. Ensure 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 Evaluation Kit and the bitstream uses the single data type.

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

Compile Network

Run the compile method of the dlhdl.Workflow object to compile the network and generate the instructions, weights, and biases for deployment.

dn = compile(hW)

### 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® Zynq® UltraScale+ MPSoC ZCU102 hardware, run the deploy method of the dlhdl.Workflow object. This method programs the FPGA board using the output of the compile method and the programming file, downloads the network weights and biases, displays progress messages, and the time it takes to deploy the network.

deploy(hW)

### 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

Test Network

Load the example image.

inputImg = imread('five_28x28.pgm');

Classify the image on the FPGA by using the predict method of the dlhdl.Workflow object and display the results.

[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

Bibliography

LeCun, Y., C. Cortes, and C. J. C. Burges. "The MNIST Database of Handwritten Digits." http://yann.lecun.com/exdb/mnist/.