GPU Code Generation for Blocks from the Deep Neural Networks Library

With GPU Coder™, you can generate optimized code for Simulink® models containing a variety of trained deep learning networks. You can implement deep learning functionality in Simulink by using MATLAB Function blocks or by using blocks from the Deep Neural Networks library of the Deep Learning Toolbox™ or the Analysis and Enhancement library from the Computer Vision Toolbox™.

GPU Coder supports the following deep learning blocks:

  • Predict (Deep Learning Toolbox) block — Predict responses using the trained network specified through the block parameter.

    For more information about working with the Predict block, see Lane and Vehicle Detection in Simulink Using Deep Learning (Deep Learning Toolbox).

  • Image Classifier (Deep Learning Toolbox) block — Classify data using a trained deep learning neural network specified through the block parameter.

    For more information about working with the Image Classifier block, see Classify ECG Signals in Simulink Using Deep Learning (Deep Learning Toolbox).

  • Stateful Classify (Deep Learning Toolbox) block — Predicts class labels for the data at the input by using the trained recurrent neural network specified through the block parameter.

  • Stateful Predict (Deep Learning Toolbox) block — Predicts responses for the data at the input by using the trained recurrent neural network specified through the block parameter.

  • Deep Learning Object Detector (Computer Vision Toolbox) block — Predicts bounding boxes, class labels, and scores for the input image by using the trained object detector specified through the block parameter.

    For more information about working with the Deep Learning Object Detector block, see Lane and Vehicle Detection in Simulink Using Deep Learning (Deep Learning Toolbox).

These library blocks enable loading of a pretrained network into the Simulink model from a MAT-file or from a MATLAB® function.

You can configure the code generator to take advantage of the NVIDIA® CUDA® deep neural network library (cuDNN) and TensorRT™ high performance inference libraries for NVIDIA GPUs. The generated code implements the deep convolutional neural network (CNN) by using the architecture, the layers, and parameters that you specify in the network object.

Example: Classify Images by Using GoogLeNet

GoogLeNet has been trained on over a million images and can classify images into 1000 object categories (such as keyboard, coffee mug, pencil, and animals). The network takes an image as input, and then outputs a label for the object in the image with the probabilities for each of the object categories. This example shows how to perform simulation and generate CUDA code for the pretrained googlenet deep convolutional neural network and classify an image. The pretrained networks are available as support packages from the Deep Learning Toolbox.

  1. Load the pretrained GoogLeNet network.

    net = googlenet;

  2. The object net contains the DAGNetwork object. Use the analyzeNetwork function to display an interactive visualization of the network architecture, to detect errors and issues in the network, and to display detailed information about the network layers. The layer information includes the sizes of layer activations and learnable parameters, the total number of learnable parameters, and the sizes of state parameters of recurrent layers.

    analyzeNetwork(net);

    Analyze network app showing network analysis of GoogLeNet.

  3. The image that you want to classify must have the same size as the input size of the network. For GoogLeNet, the size of the imageInputLayer is 224-by-224-by-3. The Classes property of the output classificationLayer contains the names of the classes learned by the network. View 10 random class names out of the total of 1000.

    classNames = net.Layers(end).Classes;
numClasses = numel(classNames);
disp(classNames(randperm(numClasses,10)))

        'speedboat'
    'window screen'
    'isopod'
    'wooden spoon'
    'lipstick'
    'drake'
    'hyena'
    'dumbbell'
    'strawberry'
    'custard apple'

Create GoogLeNet Model

  1. Create a Simulink model and insert a Predict block from the Deep Neural Networks library.

  2. Add an Image From File block from the Computer Vision Toolbox library and set the File name parameter to peppers.png. Add a Resize block from the Computer Vision Toolbox library to the model. Set the Specify parameter of the Resize block to Number of output rows and columns and enter [224 224] as the value for Number of output rows and columns. The resize block resizes the input image to that of the input layer of the network. Add a To Workspace to the model and change the variable name to yPred.

    Simulink model containing blocks for classifying images using GoogLeNet.

  3. Open the Block Parameters (subsystem) of the Predict block. Select Network from MATLAB function for Network and googlenet for MATLAB function.

  4. Connect these blocks as shown in the diagram. Save the model as googlenetModel.slx.

    Simulink model showing connection between the blocks.

Configure the Model for GPU Acceleration

Model configuration parameters determine the acceleration method used during simulation.

  1. Open the Configuration Parameters dialog box. Open the Solver pane. To compile your model for acceleration and generate CUDA code, configure the model to use a fixed-step solver. This table shows the solver configuration for this example.

    ParameterSettingEffect on Generated Code
    TypeFixed-stepMaintains a constant (fixed) step size, which is required for code generation
    Solverdiscrete (no continuous states)Applies a fixed-step integration technique for computing the state derivative of the model
    Fixed-step sizeautoSimulink chooses the step size

    Snapshot of the configuration parameters dialog showing solver options for simulation.

  2. Select the Simulation Target pane. Set the Language to C++.

  3. Select GPU acceleration. Options specific to GPU Coder are now visible in the Simulation Target > GPU Acceleration pane. For this example, you can use the default values of these parameters.

    GPU Acceleration pane on the configuration parameters dialog of the model.

  4. On the Simulation Target pane, set the Target Library in the Deep learning group to cuDNN. You can also select TensorRT.

    Snapshot of the configuration parameters dialog showing deep learning options for simulation acceleration.

  5. Click OK to save and close the Configuration Parameters dialog box.

    You can use set_param to configure the model parameter programmatically in the MATLAB Command Window.

    set_param('googlenetModel','GPUAcceleration','on');

Build GPU Accelerated Model

  1. To build and simulate the GPU accelerated model, select Run on the Simulation tab or use the command:

    out = sim('googlenetModel');

    The software first checks to see if CUDA/C++ code was previously compiled for your model. If code was created previously, the software runs the model. If code was not previously built, the software first generates and compiles the CUDA/C++ code, and then runs the model. The code generation tool places the generated code in a subfolder of the working folder called slprj/_slprj/googlenetModel.

  2. Display the top five predicted labels and their associated probabilities as a histogram. Because the network classifies images into so many object categories, and many categories are similar, it is common to consider the top-five accuracy when evaluating networks. The network classifies the image as a bell pepper with a high probability.

    im = imread('peppers.png');
predict_scores = out.yPred.Data(:,:,1);
[scores,indx] = sort(predict_scores,'descend');
topScores = scores(1:5);
classNamesTop = classNames(indx(1:5))

h = figure;
h.Position(3) = 2*h.Position(3);
ax1 = subplot(1,2,1);
ax2 = subplot(1,2,2);

image(ax1,im);
barh(ax2,topScores(1,5:-1:1,1))
xlabel(ax2,'Probability')
yticklabels(ax2,classNamesTop(5:-1:1))
ax2.YAxisLocation = 'right';
sgtitle('Top 5 predictions using GoogLeNet')

Configure Model for Code Generation

The model configuration parameters provide many options for the code generation and build process.

  1. In Configuration Parameters dialog box, select Code Generation pane. Set the System target file to grt.tlc.

    You can also use the Embedded Coder® target file ert.tlc or a custom system target file.

    For GPU code generation, the custom target file must be based on grt.tlc or ert.tlc. For information on developing a custom target file, see Customize System Target Files (Simulink Coder).

  2. Set the Language to C++.

  3. Select Generate GPU code.

  4. Select Generate code only.

  5. Select the Toolchain. For Linux® platforms, select NVIDIA CUDA | gmake (64-bit Linux). For Windows® systems, select NVIDIA CUDA (w/Microsoft Visual C++ 20XX) | nmake (64-bit windows).

    When using a custom system target file, you must set the build controls for the toolchain approach. To learn more about toolchain approach for custom targets, see Support Toolchain Approach with Custom Target (Simulink Coder).

  6. On the Code Generation > Report pane, select Create code generation report and Open report automatically.

  7. On the Code Generation > Interface pane, set the Target Library in the Deep learning group to cuDNN. You can also select TensorRT.

  8. Options specific to GPU Coder are in the Code Generation > GPU Code pane. For this example, you can use the default values of the GPU-specific parameters in Code Generation > GPU Code pane.

    GPU Code pane on the configuration parameters dialog of the model.

  9. Click OK to save and close the Configuration Parameters dialog box.

    You can also use set_param to configure the model parameter programmatically in the MATLAB Command Window.

    set_param('googlenetModel','GenerateGPUCode','CUDA');

Generate CUDA Code for the Model

  1. In the Simulink Editor, open the Simulink Coder app.

  2. Generate code.

Messages appear in the Diagnostics Viewer. The code generator produces CUDA source and header files, and an HTML code generation report. The code generator places the files in a build folder, a subfolder named googlenetModel_grt_rtw under your current working folder.

Limitations

  • GPU code generation for MATLAB Function blocks in Stateflow® charts is not supported.

  • The code generator does not support some data types from the MATLAB language. For supported data types, refer to the block documentation.

  • For GPU code generation, the custom target file must be based on grt.tlc or ert.tlc.

  • For deploying the generated code, it is recommended to use the Generate an example main program option to generate the ert_main.cu module. This option requires the Embedded Coder license.

    You can also use the rt_cppclass_main.cpp static main module provided by MathWorks®. However, the static main file must be modified such that the models class constructor points to the deep learning object. For example,

    static googlenetModelModelClass::DeepLearning_googlenetModel_T
  googlenetModel_DeepLearning;
static googlenetModelModelClass googlenetModel_Obj{ &googlenetModel_DeepLearning};

See Also

Functions

Topics