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estimatePerformance

Class: dlhdl.ProcessorConfig
Namespace: dlhdl

Retrieve layer-level latencies and performance by using estimatePerformance method

Since R2021a

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Syntax

estimatePerformance(processorConfigObject, network)
performance = estimatePerformance(processorConfigObject,network)
performance = estimatePerformance(processorConfigObject,network,Name,Value)

Description

estimatePerformance(processorConfigObject, network) returns the layer-level latencies and network performance for the object specified by the network argument.

performance = estimatePerformance(processorConfigObject,network) returns a table containing the network object layer-level latencies and performance.

performance = estimatePerformance(processorConfigObject,network,Name,Value) returns a table containing the network object layer-level latencies and performance, with one or more arguments specified by optional name-value pair arguments.

example

Input Arguments

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Processor configuration, specified as a dlhdl.ProcessorConfig object.

Name of network object for performance estimate.

Example: estimatePerformance(snet)

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Number of frames to consider for the calculation of performance estimation, specified as a positive number integer.

Example: 'FrameCount',10

Output Arguments

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Network object performance for the ProcessorConfig object, returned as a table.

Examples

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  1. Create a file in your current working folder called getLogoNetwork.m. In the file, enter:

    function net = getLogoNetwork
 if ~isfile('LogoNet.mat')
        url = 'https://www.mathworks.com/supportfiles/gpucoder/cnn_models/logo_detection/LogoNet.mat';
        websave('LogoNet.mat',url);
    end
    data = load('LogoNet.mat');
    net  = data.convnet;
end

  2. Create a dlhdl.ProcessorConfig object.

    snet = getLogoNetwork;
hPC = dlhdl.ProcessorConfig;

  3. To retrieve the layer-level latencies and performance for the LogoNet network, call the estimatePerformance method.

    hPC.estimatePerformance(snet)
    ### Notice: The layer 'imageinput' of type 'ImageInputLayer' is split into an image input layer 'imageinput' and an addition layer 'imageinput_norm' for normalization on hardware.
### Notice: The layer 'softmax' with type 'nnet.cnn.layer.SoftmaxLayer' is implemented in software.
### Notice: The layer 'classoutput' with type 'nnet.cnn.layer.ClassificationOutputLayer' is implemented in software.


              Deep Learning Processor Estimator Performance Results

                   LastFrameLatency(cycles)   LastFrameLatency(seconds)       FramesNum      Total Latency     Frames/s
                         -------------             -------------              ---------        ---------       ---------
Network                   38810151                  0.19405                       1           38810151              5.2
    ____imageinput_norm     216472                  0.00108 
    ____conv_1             6829224                  0.03415 
    ____maxpool_1          3705912                  0.01853 
    ____conv_2            10454501                  0.05227 
    ____maxpool_2          1173810                  0.00587 
    ____conv_3             9364533                  0.04682 
    ____maxpool_3          1229970                  0.00615 
    ____conv_4             1384564                  0.00692 
    ____maxpool_4            24450                  0.00012 
    ____fc_1               2644886                  0.01322 
    ____fc_2               1692534                  0.00846 
    ____fc_3                 89295                  0.00045 
 * The clock frequency of the DL processor is: 200MHz

Estimate the performance of the ResNet-18 network for multiple frames by using the dlhdl.ProcessorConfig object.

Load the ResNet-18 network and save it to net

net = resnet18;

Create a dlhdl.ProcessorConfig object and save to hPC

hPC = dlhdl.ProcessorConfig;

Retrieve layer level latencies and performance in frames per second (FPS) for multiple frames by using the estimatePerformance method with FrameNumber as an optional input argument.

hPC.estimatePerformance(net,'FrameCount',10);
### Optimizing network: Fused 'nnet.cnn.layer.BatchNormalizationLayer' into 'nnet.cnn.layer.Convolution2DLayer'
### Notice: The layer 'data' of type 'ImageInputLayer' is split into an image input layer 'data', an addition layer 'data_norm_add', and a multiplication layer 'data_norm' for hardware normalization.
### Notice: The layer 'prob' with type 'nnet.cnn.layer.SoftmaxLayer' is implemented in software.
### Notice: The layer 'ClassificationLayer_predictions' with type 'nnet.cnn.layer.ClassificationOutputLayer' is implemented in software.


              Deep Learning Processor Estimator Performance Results

                   LastFrameLatency(cycles)   LastFrameLatency(seconds)       FramesNum      Total Latency     Frames/s
                         -------------             -------------              ---------        ---------       ---------
Network                   21328236                  0.10664                      10          211208850              9.5
    ____data_norm_add       210750                  0.00105 
    ____data_norm           210750                  0.00105 
    ____conv1              2164124                  0.01082 
    ____pool1               515064                  0.00258 
    ____res2a_branch2a      966221                  0.00483 
    ____res2a_branch2b      966221                  0.00483 
    ____res2a               210750                  0.00105 
    ____res2b_branch2a      966221                  0.00483 
    ____res2b_branch2b      966221                  0.00483 
    ____res2b               210750                  0.00105 
    ____res3a_branch1       540861                  0.00270 
    ____res3a_branch2a      540749                  0.00270 
    ____res3a_branch2b      919117                  0.00460 
    ____res3a               105404                  0.00053 
    ____res3b_branch2a      919117                  0.00460 
    ____res3b_branch2b      919117                  0.00460 
    ____res3b               105404                  0.00053 
    ____res4a_branch1       503405                  0.00252 
    ____res4a_branch2a      509261                  0.00255 
    ____res4a_branch2b      905421                  0.00453 
    ____res4a                52724                  0.00026 
    ____res4b_branch2a      905421                  0.00453 
    ____res4b_branch2b      905421                  0.00453 
    ____res4b                52724                  0.00026 
    ____res5a_branch1       744525                  0.00372 
    ____res5a_branch2a      751693                  0.00376 
    ____res5a_branch2b     1415373                  0.00708 
    ____res5a                26368                  0.00013 
    ____res5b_branch2a     1415373                  0.00708 
    ____res5b_branch2b     1415373                  0.00708 
    ____res5b                26368                  0.00013 
    ____pool5                54594                  0.00027 
    ____fc1000              207351                  0.00104 
 * The clock frequency of the DL processor is: 200MHz

This example uses:

Open Live Script

This example shows how to create custom board and generate a deep learning processor IP core for the custom board. In this example you:

  • Create a custom board and reference design

  • Estimate the network performance and board resource utilization

  • Generate a custom processor and bitstream

  • Deploy the network by using the custom bitstream

The image shows the process of deploying a network to a custom board and retrieving a prediction from the deployed network.

workflow.png

This example uses the Xilinx® Kintex® UltraScale™ KCU105 board. The board contains these blocks:

  • System reset block — Used to feed the clock and reset signals to the design.

  • Memory Interface Generator (MIG) IP block — Used to generate memory controllers and interfaces for Xilinx FPGAs.

  • MATLAB JTAG AXI Manager block — Used by MATLAB to access onboard memory location. For more details, see JTAG AXI Manager (HDL Verifier).

Integrate the generated deep learning processor IP core into your reference design. For more details, see Board and Reference Design Registration System (HDL Coder).

This image shows the generated deep learning processor IP core dl_processor0 integrated into the reference design.

Register Custom Board

Define the interface and attributes of a custom SoC board. To register the Xilinx® Kintex® UltraScale™ KCU105 board:

1. Create a board registration file with the name hdlcoder_board_customization.m and add it to the MATLAB path. The hdlcoder_board_customization.m function must return a second output. For more information, see Register a Custom Board (HDL Coder).

Set the target workflow to DeepLearningProcessor. For information on other target workflows supported by HDL Coder™, see Workflows in HDL Workflow Advisor (HDL Coder).

function [boardList, workflow] = hdlcoder_board_customization
  % Board plugin registration file
  % 1. Any registration file with this name on MATLAB path will be picked up
  % 2. Registration file returns a cell array pointing to the location of 
  %    the board plugins
  % 3. Board plugin must be a package folder accessible from MATLAB path,
  %    and contains a board definition file
  %
  % Copyright 2022 The MathWorks, Inc.

boardList = { ...
     'DLKCU105.plugin_board', ...
      };
workflow = hdlcoder.Workflow.DeepLearningProcessor;
end

2. Create the board definition file. To generate a deep learning processor, you must define the ExternalMemorySize. This property defines the memory size of the DDR on the target board.

% Copyright 2022 The MathWorks, Inc.

% Board definition of KCU105
function hB = plugin_board()

% Construct board object
hB = hdlcoder.Board;

hB.BoardName    = 'Xilinx Kintex-Ultrascale KCU105 evaluation board';

% FPGA device information
hB.FPGAVendor   = 'Xilinx';
hB.FPGAFamily   = 'KintexU';
hB.FPGADevice   = 'xcku040-ffva1156-2-e';
hB.FPGAPackage  = '';
hB.FPGASpeed    = '';

% Tool information
hB.SupportedTool = {'Xilinx Vivado'};

% FPGA JTAG chain position
hB.JTAGChainPosition = 1;

% Size of external DDR memory in bytes
hB.ExternalMemorySize = 0x80000000; % 2 GB

% Add interfaces
% Standard "External Port" interface
hB.addExternalPortInterface( ...
    'IOPadConstraint', {'IOSTANDARD = LVCMOS18'});

% Custom board external I/O interface
hB.addExternalIOInterface( ...
     'InterfaceID',    'LEDs General Purpose', ...
     'InterfaceType',  'OUT', ...
     'PortName',       'GPLEDs', ...
     'PortWidth',      8, ...
     'FPGAPin',        {'AP8', 'H23', 'P20', 'P21', 'N22', 'M22', 'R23','P23'}, ...
     'IOPadConstraint', {'IOSTANDARD = LVCMOS18'});

% Custom board external I/O interface
hB.addExternalIOInterface( ...
    'InterfaceID',    'User Push Buttons', ...
    'InterfaceType',  'IN', ...
    'PortName',       'PB', ...
    'PortWidth',      1, ...
    'FPGAPin',        {'AE10'}, ...
    'IOPadConstraint', {'IOSTANDARD = LVCMOS18'});

Register Custom Reference Design

Define the interface and attributes of a custom SoC reference design. To create a custom reference design:

1. Create a reference design registration file named hdlcoder_ref_design_customization.m that contains the list of reference design plugins associated with the board. For more information, see Register a Custom Reference Design (HDL Coder).

function [rd, boardName] = hdlcoder_ref_design_customization
% Reference design plugin registration file
% 1. The registration file with this name inside of a board plugin folder 
%    will be picked up
% 2. Any registration file with this name on MATLAB path will also be picked up
% 3. The registration file returns a cell array pointing to the location of 
%    the reference design plugins
% 4. The registration file also returns its associated board name
% 5. Reference design plugin must be a package folder accessible from 
%    MATLAB path, and contains a reference design definition file
%
%   Copyright 2022 The MathWorks, Inc.

rd = {...
        'DLKCU105.matlab_3axi4_master_2020_1.plugin_rd', ...
  };

boardName = 'Xilinx Kintex-Ultrascale KCU105 evaluation board';

end

2. Create the reference design definition file. To generate a deep learning processor IP core, you must define these three AXI4 Master Interfaces:

  • AXI4 Master Activation Data

  • AXI4 Master Weight Data

  • AXI4 Master Debug

function hRD = plugin_rd()
% Reference design definition

% Copyright 2022 The MathWorks, Inc.

% Construct reference design object
hRD = hdlcoder.ReferenceDesign('SynthesisTool', 'Xilinx Vivado');

hRD.ReferenceDesignName = 'AXI-Stream DDR Memory Access : 3-AXIM';
hRD.BoardName = 'Xilinx Kintex-Ultrascale KCU105 evaluation board';

% Tool information
hRD.SupportedToolVersion = {'2020.1','2020.2','2022.1','2023.1'};

% Add custom design files

% add custom Vivado design
hRD.addCustomVivadoDesign( ...
  'CustomBlockDesignTcl', 'system_top.tcl',...
  'VivadoBoardPart',      'xilinx.com:kcu105:part0:1.0');

% Add HDL Verifier JTAG as AXI Master IP from support package
hRD.addIPRepository( ...
  'IPListFunction','hdlverifier.fpga.vivado.iplist', ...
  'NotExistMessage', 'IP Repository not found.');	

% Add interfaces

% add clock interface
hRD.addClockInterface( ...
     'ClockConnection',      'system_0/clk_out1', ...
     'ResetConnection',      'system_0/peripheral_aresetn',...
     'DefaultFrequencyMHz',  125,...
     'MinFrequencyMHz',      10,...
     'MaxFrequencyMHz',      250,...
     'ClockNumber',          1,...
     'ClockModuleInstance',  'system_0/clk_wiz_0');

% add AXI4 and AXI4-Lite slave interfaces
% This slave interface is used for intracting between DDR4 and Deep Learning IP
hRD.addAXI4SlaveInterface( ...
    'InterfaceConnection', 'system_0/M_AXI', ...
    'BaseAddress',         '0x44A00000',...
    'MasterAddressSpace',  'system_0/hdlverifier_axi_manager_0/axi4m',...
    'InterfaceType',       'AXI4');

% AXI4 Master Interface for the layer activation data with max data bit-width of 512
hRD.addAXI4MasterInterface(...
        'InterfaceID',         'AXI4 Master Activation Data', ...
        'ReadSupport',          true, ...  
        'WriteSupport',         true, ...
        'MaxDataWidth',         512, ...          
        'AddrWidth',            32, ...
        'InterfaceConnection', 'axi_interconnect_0/S01_AXI',... 
        'TargetAddressSegments', {{'ddr4_0/C0_DDR4_MEMORY_MAP/C0_DDR4_ADDRESS_BLOCK',hex2dec('80000000'),hex2dec('80000000')}}); 

% AXI4 Master Interface for the layer weight data with max data bit-width of 512
hRD.addAXI4MasterInterface(...
        'InterfaceID',         'AXI4 Master Weight Data', ...
        'ReadSupport',          true, ...  
        'WriteSupport',         true, ...
        'MaxDataWidth',         512, ...          
        'AddrWidth',            32, ...
        'InterfaceConnection', 'axi_interconnect_0/S02_AXI',... 
        'TargetAddressSegments', {{'ddr4_0/C0_DDR4_MEMORY_MAP/C0_DDR4_ADDRESS_BLOCK',hex2dec('80000000'),hex2dec('80000000')}}); 

% AXI4 Master Interface for the debugger with max data bit-width of 512
hRD.addAXI4MasterInterface(...
        'InterfaceID',         'AXI4 Master Debug', ...
        'ReadSupport',          true, ...  
        'WriteSupport',         true, ...
        'MaxDataWidth',         512, ...          
        'AddrWidth',            32, ...
        'InterfaceConnection', 'axi_interconnect_0/S03_AXI',... 
        'TargetAddressSegments', {{'ddr4_0/C0_DDR4_MEMORY_MAP/C0_DDR4_ADDRESS_BLOCK',hex2dec('80000000'),hex2dec('80000000')}});

3. The reference design plugin file must contain information about the target interface and the deep learning processor IP core, the memory address space for the deep learning processor IP core, and a command to validate the reference design. The file also requires information on the resources consumed by the reference design. This information is used during resource estimation. Add the deep learning processor information to the reference design file:

% Deep learning specific properties
hRD.registerDeepLearningTargetInterface("JTAG");
hRD.registerDeepLearningMemoryAddressSpace(0x80000000, 0x80000000); % 2GB

% Resource utilization information
hRD.ResourcesUsed.LogicElements = 30500;
hRD.ResourcesUsed.DSP = 3;
hRD.ResourcesUsed.RAM = 26.5;

Performance Estimation

Reduce the time required to design and deploy a custom deep learning network that meets performance requirements by analyzing the layer-level latencies before deploying the network.

Estimate the performance of network for your custom board by collecting calibration data from the custom board, by:

  1. Generating a calibration bitstream

  2. Deploying the calibration bitstream to the target custom board

  3. Retrieving the external to internal memory transaction latencies

Create a Processor Configuration object.

hPC = dlhdl.ProcessorConfig;

Specify the TargetPlatform. This automatically sets the SynthesisToolChipFamily, SynthesisToolDeviceName, and ReferenceDesign properties.

hPC.TargetPlatform = 'Xilinx Kintex-Ultrascale KCU105 evaluation board';

Set the target frequency.

hPC.TargetFrequency = 100;

This example uses a ResNet-18 pretrained network. For more details, see resnet18. Set the deep learning network:

net = resnet18;

To fit this design onto the target, reduce the number of parallel convolution processor kernel threads for the conv module to 9.

setModuleProperty(hPC, 'conv', 'ConvThreadNumber', 9);

Set the Xilinx Vivado toolpath to your design tool using the hdlsetuptoolpath function, then build the calibration bitstream.

hdlsetuptoolpath('ToolName','Xilinx Vivado','ToolPath','C:\Xilinx\Vivado\2020.2\bin\vivado.bat');
bitstreamPath = buildCalibrationBitstream(hPC);

Deploy the bitstream to the hardware and obtain the external- to-internal memory transaction latencies. You can use these values to get better estimates for the layer-level latencies.

deployCalibrationBitstream(hPC, bitstreamPath);

The deployCalibrationBitstream saves the calibration data from the hardware as a structure in the CalibrationData property of the dlhdl.ProcessorConfig object. The function also saves the calibration data as a MAT-file with the name calibrationData.mat. You can load this data into a new dlhdl.ProcessorConfig object by entering:

load('calibrationData.mat');
hPC.CalibrationData = calData;

Estimate the performance of the network for the custom processor configuration.

estimatePerformance(hPC, net);

MicrosoftTeams-image (1).png

Resource Estimation

Verify that the generated bistream and network fit on your target custom board, by using estimateResources to estimate the resource utilization. To learn how to estimate the resource utilization for your custom boards, see Estimate Resource Utilization for Custom Board and Reference Design.

Generate Custom Bitstream for Custom Processor Configuration

Generate a bitstream for the custom processor configuration hPC.

dlhdl.buildProcessor(hPC);

Locate the bitstream file and associated MAT file at cwd\dlhdl_prj\, where cwd is your current working folder. The name of the bitstream file is dlprocessor.bit. The name of the MAT file is dlprocessor.mat. To use the generated bitstream for the supported Xilinx boards, copy the dlprocessor.bit and dlprocessor.mat files to the current working folder.

Deploy the Custom Bitstream and Run Predictions on the Network

After you generate the bitstream, deploy the network and run the predictions on the network. For more information, refer to the Prototype Deep Learning Networks on FPGA and SoC Devices page. For an example on prototyping, see Bicyclist and Pedestrian Classification by Using FPGA.

Create Target Object

Create a target object with the vendor name of the target device. Specify the interface to connect the target device to the host using the Interface name-value pair. This example connects to the target using the JTAG interface.

hT = dlhdl.Target('Xilinx', 'Interface', 'JTAG')

Create Workflow Object for ResNet-18 Network

Create an object of the dlhdl.Workflow class. Specify the network, the bitstream name, and the target object.

hW = dlhdl.Workflow('Network', net, 'Bitstream', 'dlprocessor.bit', 'Target', hT);

Compile the Network

Run the compile function of the dlhdl.Workflow object.

compile(hW)

Deploy the Bitstream to the FPGA

To deploy the network on the Xilinx KCU105 Kintex hardware, run the deploy function of the dlhdl.Workflow object.

deploy(hW)

Run Prediction for the Network

Load the sample image.

img = imread('sampleImage1.png');
imshow(img);

Run a prediction on the image. The result output argument contains the output of the layer preceding the ClassificationOutputLayer and speed contains the profiler table.

[result, speed] = predict(hW, img, 'Profile', 'on');

MicrosoftTeams-image (2).png

Get the output class from the prediction.

[value,idx] = max(result);
classNames = net.Layers(end).Classes;
classNames(idx)

Limitations

When you estimate the performance Deep Learning HDL Toolbox™ assumes that the deep learning processor is connected to the DDR memory dedicated for the programmable logic (PL) portion of the FPGA or SoC and does not account for the DDR memory communications arbitration with other components.

Tips

To obtain the performance estimation for a dlquantizer object, set the dlhdl.ProcessorConfig object ProcessorDataType to int8.

Version History

Introduced in R2021a

See Also

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