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Profile Network for Performance Improvement

This example shows how to improve the performance of the deployed deep learning network, by identifying bottle neck layers from the profiler results.


  • Xilinx™ ZCU102 SoC development kit.

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

  • Deep Learning Toolbox™

  • Deep Learning HDL Toolbox™

Load the Pretrained SeriesNetwork

To load the pretrained digits series network, enter:

snet = getDigitsNetwork();

% To view the layers of the pretrained series network, enter:
ans = 
  15×1 Layer array with layers:

     1   'imageinput'    Image Input             28×28×1 images with 'zerocenter' normalization
     2   'conv_1'        Convolution             8 3×3×1 convolutions with stride [1  1] and padding 'same'
     3   'batchnorm_1'   Batch Normalization     Batch normalization with 8 channels
     4   'relu_1'        ReLU                    ReLU
     5   'maxpool_1'     Max Pooling             2×2 max pooling with stride [2  2] and padding [0  0  0  0]
     6   'conv_2'        Convolution             16 3×3×8 convolutions with stride [1  1] and padding 'same'
     7   'batchnorm_2'   Batch Normalization     Batch normalization with 16 channels
     8   'relu_2'        ReLU                    ReLU
     9   'maxpool_2'     Max Pooling             2×2 max pooling with stride [2  2] and padding [0  0  0  0]
    10   'conv_3'        Convolution             32 3×3×16 convolutions with stride [1  1] and padding 'same'
    11   'batchnorm_3'   Batch Normalization     Batch normalization with 32 channels
    12   'relu_3'        ReLU                    ReLU
    13   'fc'            Fully Connected         10 fully connected layer
    14   'softmax'       Softmax                 softmax
    15   'classoutput'   Classification Output   crossentropyex with '0' and 9 other classes

Create Target Object

Create a target object that has 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. For Ethernet interface, enter:

hTarget = dlhdl.Target('Xilinx','Interface','Ethernet');

To use the JTAG interface, install Xilinx™ Vivado™ Design Suite 2019.2. Set up the path to your installed Xilinx Vivado executable if it is not already set up. For example, to set the toolpath, enter:

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

For JTAG interface, enter:

% hTarget = dlhdl.Target('Xilinx','Interface','JTAG');

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 digits 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 ZCU102 SOC board. The bitstream uses a single data type.

hW = dlhdl.Workflow('Network', snet, 'Bitstream', 'zcu102_single', 'Target', hTarget);
% If running on Xilinx ZC706 board, instead of the above command, 
% uncomment the command below.
% 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"

Program Bitstream onto FPGA and Download Network Weights

To deploy the network on the Xilinx ZCU102 SoC 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.

### Programming FPGA Bitstream using Ethernet...
Downloading target FPGA device configuration over Ethernet to SD card ...
# Copied /tmp/hdlcoder_rd to /mnt/hdlcoder_rd
# Copying Bitstream hdlcoder_system.bit to /mnt/hdlcoder_rd
# Set Bitstream to hdlcoder_rd/hdlcoder_system.bit
# Copying Devicetree devicetree_dlhdl.dtb to /mnt/hdlcoder_rd
# Set Devicetree to hdlcoder_rd/devicetree_dlhdl.dtb
# Set up boot for Reference Design: 'AXI-Stream DDR Memory Access : 3-AXIM'

Downloading target FPGA device configuration over Ethernet to SD card done. The system will now reboot for persistent changes to take effect.

System is rebooting . . . . . .
### Programming the FPGA bitstream has been completed successfully.
### Loading weights to FC Processor.
### FC Weights loaded. Current time is 28-Jun-2020 12:24:21

Load Example Image

Load the example image.

inputImg = imread('five_28x28.pgm');

Run the Prediction

Execute the predict function of the dlhdl.Workflow object that has profile option set to 'on' to display the latency and throughput results.

[~, 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                      73231                  0.00033                       1              73273           3002.5
    conv_module              26847                  0.00012 
        conv_1                6618                  0.00003 
        maxpool_1             4823                  0.00002 
        conv_2                4876                  0.00002 
        maxpool_2             3551                  0.00002 
        conv_3                7039                  0.00003 
    fc_module                46384                  0.00021 
        fc                   46384                  0.00021 
 * The clock frequency of the DL processor is: 220MHz

Identify and Display the Bottle Neck Layer

Remove the NumFrames, Total latency, and Frames/s from the profiler's results table. This includes removing the module level and network level profiler results. Retain only the network layer profiler results. Once the bottle neck layer has been identified display the bottle neck layer index, running time, and information.

speed('Network',:) = [];
speed('____conv_module',:) = [];
speed('____fc_module',:)  = [];
speed = removevars(speed, {'NumFrames','Total Latency(cycles)','Frame/s'});

% then sort the profiler's results in descending ordering
speed = sortrows(speed,'Latency(cycles)','descend');

% the first row in the profile table is the bottleneck layer. Thus the
% following 
layerSpeed = speed(1,:);
layerName = strip(layerSpeed.Properties.RowNames{1},'_');
for idx = 1:length(snet.Layers)
    currLayer = snet.Layers(idx);
    if strcmp(currLayer.Name, layerName)
        bottleNeckLayer = currLayer;

% disply the bottle neck layer index 
dnnfpga.disp(['Bottleneck layer index is ', num2str(idx), '.']);
### Bottleneck layer index is 13.
% disply the bottle neck layer running time percentage  
percent = layerSpeed.("Latency(cycles)")/sum(speed.("Latency(cycles)")) * 100;
dispStr = sprintf('It accounts for about %0.2f percent of the total running time.', percent);
### It accounts for about 63.29 percent of the total running time.
% disply the bottle neck layer information  
dnnfpga.disp('Bottleneck layer information: ');
### Bottleneck layer information: 
  FullyConnectedLayer with properties:

          Name: 'fc'

     InputSize: 1568
    OutputSize: 10

   Learnable Parameters
       Weights: [10×1568 single]
          Bias: [10×1 single]

  Show all properties