# Build Larq Compute Engine for ARM¶

This page descibes how to build Larq Compute Engine (LCE) binaries for 32-bit, as well as 64-bit ARM-based systems. Bazel is the primary build system for LCE and can be used to cross-compile binaries for ARM architectures directly from the host. To natively build on an ARM system, we provide a solution based on the Makefile build system.

This leaves us with three ways to build LCE binaries, which we recommend in the following order:

1. To cross-compile LCE from a host machine, see Cross-compiling LCE with Bazel
2. To natively compile LCE, see Building LCE with Make.
3. To cross-compile LCE using the Make system for users that do not wish to install Bazel, see Cross-compiling LCE with Make.

This guide will show you how to build the LCE example program. See here to find out how you can create your own LCE inference binary.

NOTE: Although the Raspberry Pi 3 and Raspberry Pi 4 have 64-bit CPUs, the officially supported OS Raspbian for the Raspberry Pi is a 32-bit OS. In order to use the optimized 64-bit kernels of LCE on a Raspberry Pi, a 64-bit OS such as Manjaro should be used.

## Cross-compiling LCE with Bazel¶

First configure Bazel using the instructions here.

To cross-compile the LCE example for ARM architectures, the bazel target needs to be built with the --config=rpi3 (32-bit ARM) or --config=aarch64 (64-bit ARM) flag. For example, to build the example for 64-bit ARM systems, run the following command from the LCE root directory:

bazel build \
--config=aarch64 \
//examples:lce_minimal


To build the LCE benchmark tool, build the bazel target //larq_compute_engine/tflite/benchmark:lce_benchmark_model

The resulting binaries will be stored at bazel-bin/examples/lce_minimal and bazel-bin/larq_compute_engine/tflite/benchmark/lce_benchmark_model. You can copy these to your ARM machine and run them there.

## Building LCE with Make¶

To build LCE with Make, first clone the Larq Compute Engine repo and make sure the tensorflow submodule is loaded (this only has to be done once):

git submodule update --init


To simplify the build process for various supported targets, we provide the build_lce.sh script which accepts the build target platform as an input argument.

To natively build the LCE library and C++ example programs, first you need to install the compiler toolchain on your target device. On Debian based systems like a Raspberry Pi board with Raspbian, run the following command:

sudo apt-get install build-essential


On an Arch based system like a Raspberry Pi board with Manjaro operating system, run the following command instead:

sudo pacman -S base-devel


You should then be able to natively compile LCE by running the following from the LCE root directory:

larq_compute_engine/tflite/build_make/build_lce.sh --native


It is also possible to replace --native by --rpi (32-bit ARM) or --aarch64 (64-bit ARM) to add extra compiler optimization flags.

The resulting compiled files will be stored in third_party/tensorflow/tensorflow/lite/tools/make/gen/<TARGET> where, depending on your target platform, <TARGET> can be linux_x86_64, rpi_armv7l, or aarch64_armv8-a. In the bin folder, you can find the example program lce_minimal and benchmark program benchmark_model. In the lib folder, you can find the TensorFlow Lite static library libtensorflow-lite.a which includes the LCE customs ops.

Note

On some systems the compiler is incorrectly named aarch64-unknown-linux-gnu-gcc while it should be named aarch64-linux-gnu-gcc. If building with the option --aarch64 results in errors then the following bash script can be used to create symlinks that fix this naming issue.

#!/usr/bin/env bash

cd /usr/bin
for unknownfile in aarch64-unknown-linux-gnu-*; do
newfile="${unknownfile/-unknown-/-}" echo "Creating symlink$newfile that points to $unknownfile" ln -s$unknownfile \$newfile
done


## Cross-compiling LCE with Make¶

First clone the Larq Compute Engine repo and make sure the tensorflow submodule is loaded (this only has to be done once):

git submodule update --init


To cross-compile LCE, you need to first install the compiler toolchain. For Debian based systems, run the following commands:

sudo apt-get update
sudo apt-get install crossbuild-essential-armhf


On an Arch based systems, the package is called arm-linux-gnueabihf:

sudo pacman -Syy
sudo pacman -S arm-linux-gnueabihf


To build for 32-bit ARM architectures, run the following command from the LCE root directory:

larq_compute_engine/tflite/build_make/build_lce.sh --rpi


When building for a 64-bit ARM architecture, replace --rpi with --aarch64.

See Building LCE with Make for the location of the resulting build files. Copy the benchmark_model program to your ARM machine to run it.