Get Started with Deep Learning FPGA Deployment on Intel Arria 10 SoC

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

Prerequisites

Intel Arria™ 10 SoC development kit
Deep Learning HDL Toolbox™ Support Package for Intel FPGA and SoC
Deep Learning HDL Toolbox™
Deep Learning Toolbox™

Create a Folder and Copy Relevant Files

Create a new folder in your current working folder where you have write permission and copy all the files into this folder.

unzip('dnnfpga_digits.zip');
[newDir, origDir] = cloneSetupDir('dnnfpga_digits');
cd(newDir);

Load the Pretrained SeriesNetwork

To load the pretrained series network, that has been trained on the Modified National Institue Standards of Technolofy (MNIST) database, enter:

snet = getDigitsNetwork();

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

analyzeNetwork(snet)

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. To use JTAG, install Intel™ Quartus™ Prime Standard Edition 18.1. Set up the path to your installed Intel Quartus Prime executable if it is not already set up. For example, to set the toolpath, enter:

% hdlsetuptoolpath('ToolName', 'Altera Quartus II','ToolPath', 'C:\altera\18.1\quartus\bin64');

hTarget = dlhdl.Target('Intel')

hTarget = 
  Target with properties:

       Vendor: 'Intel'
    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 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 Intel Arria 10 SOC board and the bitstream uses a single data type.

hW = dlhdl.Workflow('network', snet, 'Bitstream', 'arria10soc_single','Target',hTarget)

hW = 
  Workflow with properties:

            Network: [1×1 SeriesNetwork]
          Bitstream: 'arria10soc_single'
    ProcessorConfig: []
             Target: [1×1 dlhdl.Target]

Compile the 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 Intel Arria 10 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. The deploy function starts programming the FPGA device, 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 15:19:17

Run Prediction for Example Image

To load the example image, execute the predict function of the dlhdl.Workflow object, and then display the FPGA result, enter:

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

Run prediction with the profile 'on' to see the latency and throughput 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                      49438                  0.00033                       1              49671           3019.9
    conv_module              26288                  0.00018 
        conv_1                6741                  0.00004 
        maxpool_1             4680                  0.00003 
        conv_2                5231                  0.00003 
        maxpool_2             3879                  0.00003 
        conv_3                5817                  0.00004 
    fc_module                23150                  0.00015 
        fc                   23150                  0.00015 
 * The clock frequency of the DL processor is: 150MHz

[val, idx] = max(prediction);
fprintf('The prediction result is %d\n', idx-1);

The prediction result is 5

cd(origDir);

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