MATLAB: Does MATLAB coder R2018b support SVM algorithms for code generation on to an ARM target that has the ARM Compute library installed on it

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Does MATLAB coder (R2018b) support SVM algorithms for code generation on to an ARM target that has the ARM Compute library installed on it?

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MATLAB Coder generates ANSI/ISO C and C++ code that can be compiled and executed on any processor by manually integrating the generated code with the RTOS and build tools for your processor

Matlab Coder in R2018b supports both regression and classification algorithms for code generation. The following documentation page details the different Statistics and Machine Learning Toolbox functions that are supported for code generation using MATLAB Coder:

https://www.mathworks.com/help/releases/R2018b/coder/ug/functions-supported-for-code-generation-categorical-list.html#btwz3ma

To build and run generated C++ code for Deep Learning on an ARM hardware target, you must have the ARM Compute Library installed on the ARM target. For the MATLAB Coder R2018b release, use ARM Compute version 18.03. Additional details on how to build the ARM Compute library on an ARM target and how to set up environment variables on your ARM target to point to the ARM Compute Library can be found on the following MATLAB answers page:

https://www.mathworks.com/matlabcentral/answers/455590-matlab-coder-how-do-i-build-the-arm-compute-library-for-deep-learning-c-code-generation-and-deplo

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MATLAB: MATLAB Coder: How to build the ARM Compute Library for Deep Learning C++ code generation and deployment

Background

To build and run generated C++ code for Deep Learning on an ARM hardware target, you must have the ARM Compute Library installed on the ARM target.

The following describes instructions for building the ARM Compute library on an ARM target, such as the Raspberry Pi, Hikey960, and so on. You must use the ARM Compute Library version appropriate for your MATLAB Coder release:

For the MATLAB Coder R2018b release, use ARM Compute version 18.03.
For the MATLAB Coder R2019a release, you can use ARM Compute version 18.03 or 18.05.
For other MATLAB Coder releases, use the ARM Compute version specific for the release, as described in the release documentation: https://www.mathworks.com/help/coder/ug/prerequisites-for-deep-learning-with-matlab-coder.html

If you need to set up environment variables on your ARM target to point to the ARM Compute Library, see:

How do I setup the environment variables on ARM targets to point to the ARM Compute Library?

ARM Compute Library Build Instructions on Linux based hardware

These instructions assume that the operating system is already present on the hardware. For example, Raspbian Stretch on the Raspberry Pi. On the target hardware, open a command terminal and perform these steps.

1. Install git. Enter:

sudo apt-get install git

2. As noted above, you must use the ARM Compute Library appropriate for your MATLAB Coder release. To download the latest version of the ARM Compute library from https://github.com/ARM-software/ComputeLibrary, enter:

git clone https://github.com/Arm-software/ComputeLibrary.git

To use older libraries:

a. Download source code zip or tar file from https://github.com/ARM-software/ComputeLibrary/releases and unzip downloaded source code. (OR)

b. Use git commands to download a specific version. For example, to download version 18.05, use below commands:

git clone https://github.com/Arm-software/ComputeLibrary.git
cd ComputeLibrary
git tag -l
git checkout v18.05

3. Install scons:

sudo apt-get install scons
cd ComputeLibrary

4. Build the library by running the scons command with the relevant build options. For information on library build options, see https://arm-software.github.io/ComputeLibrary/latest/index.xhtml#S3_how_to_build. The ARM Compute libraries are generated under the folder 'build'.

For example, to build the ARM Compute Library for Raspberry Pi, enter:

scons Werror=0 -j2 debug=0 neon=1 opencl=0 os=linux arch=armv7a openmp=1 examples=0 asserts=0 build=native

To build ARM Compute Library for Hikey960 with opencl, enter:

scons Werror=0 -j2 debug=0 neon=0 opencl=1 os=linux arch=arm64-v8a openmp=1 examples=0 asserts=0 build=native

5. Rename the folder ‘build’ to ‘lib’.

MATLAB: What embedded compilers and Integrated Development Environments (IDE) does Embedded Coder support

Embedded Coder generates ANSI/ISO C/C++ code and thus supports all embedded compilers and IDEs that support C/C++ code. If portable ANSI/ISO C/C++ that can be called by any scheduler is not sufficient, Embedded Coder has a variety of options and features that end users can leverage to generate target specific code for their particular compiler environment. A sample of these options and features are listed below.

Optimize Generated Code by Developing and Using Code Replacement Libraries - MATLAB and Simulink:

https://www.mathworks.com/help/ecoder/examples/optimize-generated-code-by-developing-and-using-code-replacement-libraries-mc.html

https://www.mathworks.com/help/ecoder/examples/optimize-generated-code-by-developing-and-using-code-replacement-libraries-sc.html

Custom Simulink Real-Time drivers (R2017b and earlier releases):

https://www.mathworks.com/help/releases/R2017b/xpc/driver/introduction.html

Memory/Pragma Sections:

https://www.mathworks.com/help/ecoder/ug/control-data-and-function-placement-in-memory-by-inserting-pragmas.html

Legacy Code Integration using Legacy Code Tool:

https://www.mathworks.com/help/simulink/legacy-code-integration.html

Control generation of Function Prototypes:

https://www.mathworks.com/help/ecoder/ug/support-c-function-prototype-control.html

Code Generation from MATLAB and Simulink Code:

https://www.mathworks.com/help/ecoder/c-c-code-generation-from-matlab-code.html

https://www.mathworks.com/help/ecoder/c-c-code-generation-from-simulink-models.html

MATLAB Central contains several examples that may also be relevant to your needs:

<https://www.mathworks.com/matlabcentral/fileexchange/?term=Embedded+coder+type%3A%22Example%22&utf8=%E2%9C%93>

Note that MathWorks does not guarantee or warrant the use or content of MATLAB Central submissions. Any questions, issues, or complaints should be directed to the contributing author.

In addition to ANSI C/C++ code generation and target customization options mentioned above, MathWorks and third party link and target products offer built-in integration support for several target, compiler, and RTOS combinations. These products provide device driver blocks, target optimized code, build environment automation, Processor-in-Loop testing, and other features.

You can find all the relevant details related to Embedded Coder in the below link:

https://www.mathworks.com/products/embedded-coder.html

Finally, MathWorks consulting and a number of third parties service providers are able to create custom solutions for your target environment needs.

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Related Solutions

MATLAB: MATLAB Coder: How to build the ARM Compute Library for Deep Learning C++ code generation and deployment

MATLAB: What embedded compilers and Integrated Development Environments (IDE) does Embedded Coder support

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