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

I see a few deep learning networks supported for code generation using MATLAB Coder:

• Deep Learning Networks and Layers Supported for C++ Code Generation

I'm looking to generate code from my deep learning network (like AlexNet, GoogLeNet, ResNet, SqueezeNet, VGG-16/19, etc) to run on ARM Cortex-A processors using MATLAB Coder and the ARM Compute Library. What are the steps to do this?

Answer 1

[Bill Chou]

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/v20.02.1/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’.

[Riccardo Vocale]

when i try to bild my model on simulink i get this error

Unable to find Arm Compute library for selected version. To install ARM compute library run the Hardware Setup process.

how can i link the arm folder to matlab? i am using android device