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

Asked by Bill Chou
on 10 Apr 2019
Latest activity Edited by Bill Chou
on 10 Jul 2019
I see a few deep learning networks supported for code generation using MATLAB Coder:
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?

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1 Answer

Answer by Bill Chou
on 10 Apr 2019
Edited by Bill Chou
on 10 Jul 2019
 Accepted Answer

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

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