coder.ARMNEONConfig

Parameters to configure deep learning code generation with the ARM Compute Library

Description

The coder.ARMNEONConfig object contains ARM^® Compute Library and target specific parameters that codegen uses for generating C++ code for deep neural networks.

To use a coder.ARMNEONConfig object for code generation, assign it to the DeepLearningConfig property of a code generation configuration object that you pass to codegen.

Creation

Create an ARM NEON configuration object by using the coder.DeepLearningConfig function with target library set as 'arm-compute'.

Properties

ArmComputeVersion — Version of ARM Compute Library

'20.02.1' (default) | '19.05'

Version of ARM Compute Library used on the target hardware, specified as a character vector or string scalar. For more information, see ARM Compute Library version.

ArmArchitecture — ARM architecture supported in the target hardware

'armv8' | 'armv7'

ARM architecture supported in the target hardware, specified as a character vector or string scalar. For more information, see ARM Compute architecture.

DataType — Inference computation precision

'fp32' (default) | 'int8'

Specify the precision of the inference computations in supported layers. For more information, see Data type (ARM Compute).

CalibrationResultFile — Location of calibration MAT file

'' (default) | character vector | string scalar

Location of the MAT file containing the calibration data. This option is applicable only when DataType is set to 'int8'. For more information, see Calibration result file path (ARM Compute).

TargetLibrary — Target library name

'arm-compute'

Name of target library, specified as a character vector.

Examples

Generate Code for the ResNet-50 Network Using ARM Compute Library

Create an entry-point function resnet_predict that uses the coder.loadDeepLearningNetwork function to load the dlnetwork object that contains the SqueezeNet network. For more information, see Code Generation for dlarray.

function out = resnet_predict(in)

dlIn = dlarray(in, 'SSCB');
persistent dlnet;
if isempty(dlnet)
    dlnet = imagePretrainedNetwork('resnet50');
end

dlOut = predict(dlnet, dlIn);
out = extractdata(dlOut);

Create a coder.config configuration object for generation of a static library.

cfg = coder.config('lib');

Set the target language to C++. Specify that you want to generate only source code.

cfg.TargetLang = 'C++';
cfg.GenCodeOnly=true;

Create a coder.ARMNEONConfig deep learning configuration object. Assign it to the DeepLearningConfig property of the cfg configuration object.

dlcfg = coder.DeepLearningConfig('arm-compute');
dlcfg.ArmArchitecture = 'armv8';
dlcfg.ArmComputeVersion = '20.02.1';
cfg.DeepLearningConfig = dlcfg;

Use the -config option of the codegen function to specify the cfg configuration object. The codegen function must determine the size, class, and complexity of MATLAB^® function inputs. Use the -args option to specify the size of the input to the entry-point function.

codegen -args {ones(227,227,3,'single')} -config cfg squeezenet_predict

The codegen command places all the generated files in the codegen folder. The folder contains the C++ code for the entry-point function squeezenet_predict.cpp, header, and source files containing the C++ class definitions for the neural network, weight, and bias files.

Version History

Introduced in R2019a