dlhdl.Processor Class

Namespace: dlhdl

Configure processor object to handle deep learning processor IP core input and output data

Since R2023b

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Description

Use objects of the dlhdl.Processor class to manage the input and output data from the deep learning processor IP core.

Creation

hProc = dlhdl.Processor(Network=Network,ProcessorConfig=ProcessorConfig) creates a processor object for the deep learning network, Network, and the processor configuration, ProcessorConfig.

Input Arguments

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Deep learning network object, specified as a SeriesNetwork, DAGNetwork, or dlnetwork object.

Example: Network=resnet18

Processor configuration, specified as a dlhdl.ProcessorConfig object.

Example: ProcessorConfig=dlhdl.ProcessorConfig

Methods

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Examples

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Create a network with an input layer of size 10-by-10-by-5.

layers = [imageInputLayer([10,10,5],'Normalization','none')
convolution2dLayer(3,5,'Padding','same')
regressionLayer];
layers(2).Weights = ones(3,3,5,5);
layers(2).Bias = ones(1,1,5);
net = assembleNetwork(layers);

Create a processor configuration object and set the convolution thread number as nine.

hPC = dlhdl.ProcessorConfig;
hPC.setModuleProperty('conv','ConvThreadNumber',9);
hPC = 

                    Processing Module "conv"
                            ModuleGeneration: 'on'
                          LRNBlockGeneration: 'off'
                 SegmentationBlockGeneration: 'on'
                            ConvThreadNumber: 9
                             InputMemorySize: [227 227 3]
                            OutputMemorySize: [227 227 3]
                            FeatureSizeLimit: 2048

                      Processing Module "fc"
                            ModuleGeneration: 'on'
                      SoftmaxBlockGeneration: 'off'
                              FCThreadNumber: 4
                             InputMemorySize: 25088
                            OutputMemorySize: 4096

                  Processing Module "custom"
                            ModuleGeneration: 'on'
                                     Sigmoid: 'off'
                                   TanhLayer: 'off'
                                    Addition: 'on'
                                   MishLayer: 'off'
                              Multiplication: 'on'
                                    Resize2D: 'off'
                                  SwishLayer: 'off'
                             InputMemorySize: 40
                            OutputMemorySize: 120

              Processor Top Level Properties
                              RunTimeControl: 'register'
                               RunTimeStatus: 'register'
                          InputStreamControl: 'register'
                         OutputStreamControl: 'register'
                                SetupControl: 'register'
                           ProcessorDataType: 'single'

                     System Level Properties
                              TargetPlatform: 'Xilinx Zynq UltraScale+ MPSoC ZCU102 Evaluation Kit'
                             TargetFrequency: 200
                               SynthesisTool: 'Xilinx Vivado'
                             ReferenceDesign: 'AXI-Stream DDR Memory Access : 3-AXIM'
                     SynthesisToolChipFamily: 'Zynq UltraScale+'
                     SynthesisToolDeviceName: 'xczu9eg-ffvb1156-2-e'
                    SynthesisToolPackageName: ''
                     SynthesisToolSpeedValue: ''

Create a processor object and a random input array of size 10-by-10-by-5.

hProc = dlhdl.Processor(Network=net,ProcessorConfig=hPC);
im = rand(10,10,5);
hProc = 

  Processor with properties:

            Network: [1×1 SeriesNetwork]
    ProcessorConfig: [1×1 dnnfpga.config.CNN5ProcessorConfig]

Retrieve the padded input data by using the getExpectedPaddedInputData method. The size of the output matrix is 10-by-10-by-8. Reshape output to an 800-by-1 matrix.

output = getExpectedPaddedInputData(hProc,im);
paddedData = dnnfpga.format.convert3DInputToDDRVectorFormatConv4(output, 4);

Retrieve the unpadded output data by using getUnpaddedOutputData. The size of the unpadded output data corresponds to the size of the conv layer, which is 10-by-10-by-5.

outH = getUnpaddedOutputData(hProc,paddedData',1,'conv');

Version History

Introduced in R2023b

See Also

Topics