Cross-compiling allows you to build code for a target platform different from the build host. For ARM Cortex-M4, this means building 32-bit code on a 64-bit x86 host computer. Cross-compiling can provide performance advantages over native compiling and allows developing for hardware you don’t have locally.
Introduction to Cross-Compiling
Cross-compiling involves using a compiler that runs on one platform, like x86, to generate code for another, like ARM. This allows developers to build for hardware they don’t have access to. The build tools run natively on the host, while only the compilation stage targets the other architecture.
For example, you can cross-compile on an x86 desktop to target an ARM device like a Cortex-M4 microcontroller. This allows creating and testing code before deploying to the target. It also leverages the greater performance of x86 hosts over less powerful embedded devices.
Advantages of Cross-Compiling
- Faster build times – Building natively on x86 is much quicker than lower power ARM devices
- No need for target hardware – Develop applications before target devices available
- Centralized development – Standard toolchain for entire team
- Advanced host tools – Leverage more capable editors, debuggers, etc
Disadvantages of Cross-Compiling
- More complex setup – Requires installing cross compiler and configuring build
- Limitations testing – Cannot natively run apps on host to test
- Debugging obstacles – Cannot easily debug on host machine
- Platform differences – Build environment differs from target, risks mismatches
Cross-Compiling Process Overview
At a high level, cross-compiling involves three main steps:
- Install a cross compiler targeting the desired architecture and configure your development environment.
- Build the source code using the cross compiler instead of a native compiler.
- Deploy and test the compiled binary on the target system.
The cross compiler builds executables and libraries intended to run on the target hardware. It converts source code like C/C++ to binary code compatible with the target architecture.
Cross-compiling requires setting up the development toolchain to use the appropriate cross compiler. You also need a way to transfer the compiled binaries to the target system for testing and validation.
Choosing a Cross Compiler
The first step in cross-compiling is choosing a suitable cross compiler for your target architecture. For Cortex-M4, common choices include:
- GNU Arm Embedded Toolchain – Free software option from Arm’s GNU toolchain project
- Arm Compiler – Proprietary Arm compiler included with Arm Development Studio
- Clang/LLVM – Open source compiler with Arm support
The GNU and Clang/LLVM toolchains are free open source options. The Arm Compiler is a commercial solution but provides advanced optimizations.
Key considerations when selecting a cross compiler:
- Cost – Balance of proprietary vs open source tools
- Platform support – Host and target OS combinations
- Performance – Compilation speed and code efficiency
- Features – Debugging, profiling, and other capabilities
- Ease of use – Integration with IDEs and build systems
- Licensing – Open source vs commercial restrictions
GNU Arm Embedded Toolchain
The GNU Arm Embedded toolchain is a popular open source option for Cortex-M devices. It supports Arm Cortex-M and Cortex-R processor families. The toolchain is distributed by Arm’s GNU toolchain project and runs on Linux, MacOS, and Windows hosts.
It includes the GCC compiler, GDB debugger, and additional utilities for Arm development. It supports C, C++, and assembly programming languages. The toolchain generates optimized code for various Arm architecture profiles.
Advantages of the GNU Arm toolchain include being free, simple to set up, and integrating well with IDEs. Compilation speed is decent, although the Arm Compiler can provide better optimizations.
Arm Compiler is a commercial C, C++, and assembly compiler from Arm. It offers highly optimized code generation targeting Arm architectures. The compiler is standards compliant and integrates tightly with Arm Development Studio.
Key features of Arm Compiler include fast compile times, advanced optimizations, and support for NEON SIMD instructions. It also connects seamlessly to Arm’s debuggers and profiling tools. Licensing is required but low-cost student licenses are available.
The Arm Compiler provides extremely efficient code compared to GCC. However, being proprietary comes with increased cost and usage restrictions to consider.
The Clang/LLVM compiler is an open source alternative to GCC. It aims to provide faster compilation while improving error and warning messages compared to GCC.
Clang supports many Arm architectures including Cortex-M with the Arm Compute Library. It is used alongside the LLVM toolchain which includes linker, assembler, and other utilities.
Benefits include fast incremental compilations and modern language support. It is open source with permissive licensing. However, Arm support is still maturing with fewer optimizations versus GCC or Arm’s compiler.
Installing a Cross Compiler
Once you select a cross compiler, the next step is installation and configuration. The process varies by toolchain, but often involves:
- Downloading the compiler binaries for your host platform
- Extracting the archives to a folder on your system
- Adding the compiler binaries to your PATH
- Configuring environment variables for cross-compiling
This makes the cross compiler tools accessible to your build system and configures them to target Arm Cortex-M4. Install directions are provided for each compiler.
Configuring GCC Path
As an example, GNU Arm Embedded Toolchain binaries for Linux/MacOS are distributed as a compressed tar archive file. After extracting the archive, you need to add the
bin folder to your PATH to access the tools:
export PATH=<install folder>/gcc-arm-none-eabi-10-2020-q4-major/bin:$PATH
You also need to set the
ARM_PATH variable pointing to the toolchain root directory:
export ARM_PATH=<install folder>/gcc-arm-none-eabi-10-2020-q4-major
This makes the cross compiler available to the build system when invoking
Verifying the Cross Compiler
To validate the compiler is installed correctly, you can print the GCC version:
This should output details on the GNU Arm Embedded compiler version and target architecture:
arm-none-eabi-gcc (GNU Arm Embedded Toolchain 10-2020-q4-major) 10.2.1 20201103 (release) Copyright (C) 2020 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
With the cross compiler installed, the next step is configuring your build system to use it instead of the default native compiler.
Configuring the Build Environment
Once the cross compiler is installed, you need to integrate it with your build system. This involves configuring the C/C++ compiler and linker tools to use the equivalent cross tools instead of native versions.
For example, instead of
gcc you will use
arm-none-eabi-ld instead of the
ld linker. The same applies for other utilities like the assembler and debugger.
Build systems like Make, CMake, SCons, etc provide configuration options to set the cross compiler. For example, CMake uses variables like
CMAKE_C_COMPILER to specify the C compiler.
For Makefiles, you need to set
LD to point to the cross tools instead of native compilers:
CC = arm-none-eabi-gcc LD = arm-none-eabi-ld
Any compiler flags for things like preprocessor definitions and include paths also need updated for cross-compiling:
CFLAGS = -mcpu=cortex-m4 -mthumb -mfloat-abi=hard -mfpu=fpv4-sp-d16
Make sure to specify the correct target architecture and ABI options.
CMake Toolchain File
For CMake, you can create an Arm toolchain file to set the cross compilers. For example:
SET(CMAKE_SYSTEM_NAME Generic) SET(CMAKE_C_COMPILER arm-none-eabi-gcc) SET(CMAKE_CXX_COMPILER arm-none-eabi-g++) SET(CMAKE_ASM_COMPILER arm-none-eabi-gcc) SET(CMAKE_OBJCOPY arm-none-eabi-objcopy CACHE INTERNAL "")
This configures CMake to use the Arm cross compiler tools. You specify this file at generate time:
cmake -DCMAKE_TOOLCHAIN_FILE=<toolchain file>
Cross Compiling Code
After configuring your build system to use the cross compilers, you can start compiling code. The process for building is essentially the same as for native compiling.
For example, a typical build workflow would be:
- Write code in C/C++, assembly, etc
- Build libraries and object files from source using cross compiler
- Link objects and libraries to produce executable
- Inspect compilation results for errors and warnings
- Repeat until a satisfactory binary is produced
The key difference is the cross compiler generates non-native binaries. The resulting executable can then be deployed to the target Arm device.
A sample build process using Make and GCC might look like:
arm-none-eabi-gcc -c -o main.o main.c arm-none-eabi-gcc -o main.elf main.o
This compiles the source files into objects then links an executable ELF binary. The same build process works for compiling projects with multiple source and header files into libraries and executables.
Configuring optimizer flags is important for performance on microcontrollers like Cortex-M4. Some possible GCC optimizations include:
-O1 Enable optimizations -Os Optimize for size -mfpu=fpv4-sp-d16 Optimize for hardware FPU -mcpu=cortex-m4 Optimize for Cortex-M4 -mthumb Generate Thumb code -march Optimize for architecture
Optimizations improve code efficiency which is critical for resource constrained devices. Be sure to test that aggressive optimizations do not result in incorrect behavior.
Debug vs Release
As with native development, different compiler configurations are useful for debug versus release builds. Debug configs disable optimizations and include debug symbols:
-O0 Disable optimizations -g Include debug symbols -ggdb Generate debugger-friendly output
Release builds optimize for performance and size while stripping debug symbols:
-Os Optimize for size -flto Enable link-time optimizations -s Strip symbols
Profiling on target hardware can help balance optimization levels versus debugability.
Deploying to Target Hardware
After cross-compiling code, the next step is deploying it to target devices for testing. This requires a mechanism to transfer the binary to the hardware.
For debugging and development, the ARM Cortex Microcontroller Debug Interface (MCD) is useful. This allows flashing and debugging via probes like J-Link and ST-Link connected over SWD, JTAG, or USB.
Debug probes integrate with IDEs like Eclipse, VSCode, etc to flash binaries. They also support stepping through code and inspecting registers and memory state.
For production, common approaches to load code include:
- Flash loaders or bootloaders – upload new firmware over UART, USB, Ethernet, etc
- External flash – program external memories with updater utility
- ROM bootloaders – flash internal ROM via hardware programmers
Bootloaders or flash loaders allow updating firmware directly on devices. External flash chips can be reprogrammed separately from microcontroller. ROM bootloaders require physical reflashing using JTAG or SWD programmers.
Testing and Debugging
After deploying to hardware, the next step is testing the application and debugging any issues. Debugging cross-compiled code brings unique challenges.
For testing, logging bugs is extremely helpful since you cannot run code natively. Effective logging includes:
- Printing output over UART, USB serial, etc
- Dumping processor registers and stack traces on failures
- Tracking down hard faults and segmentation faults
- Monitoring task states, events, resource usage, etc
Granular logging allows diagnosing issues only visible on hardware and not the development host.
For interactive debugging, probes like J-Link allow using GDB and IDE debuggers remotely. Features like breakpoints, watchpoints, and register inspection are indispensable.
Make sure any debugger configurations match the cross-compiled binaries. The debugger needs awareness of the target architecture to track state properly.
Once bugs are fixed, regression testing helps prevent regressions on future changes. Some techniques include:
- Unit tests for module interfaces
- Integration tests across components
- Simulation tests for corner cases
- Automated testing framework
Start testing early in development to capture requirements and constraints needed for the target hardware and application.
Tuning for Target Architecture
One cross-compiling benefit is leveraging capabilities of the target not available on the host. For Cortex-M4, key optimizations include:
- Thumb-2 instruction set – Improves code density
- Hardware FPU – Accelerate floating point computations
- SIMD instructions – Optimize multimedia workloads
- DSP extensions – Speed up digital signal processing
- Coprocessors – Offload processing to dedicated hardware
- Caches – Faster access to frequently used data
- Bus fabric – Choose appropriate interconnect for peripherals
- Memory topology – Optimize based on memory types/speeds
Profiling on the hardware can guide appropriate tradeoffs between size, speed, and power consumption depending on workload requirements.
Thumb-2 Instruction Set
The Thumb-2 instruction set provides 32-bit instructions while maintaining a high code density crucial for embedded applications. The compact 16-bit and 32-bit encodings result in smaller code than regular ARM instructions.
Thumb-2 includes many 32-bit instructions compatible with the ARM instruction set. This allows mixing 16-bit and 32-bit instructions to balance code density versus performance.
The Cortex-M4 processor includes an optional single precision hardware FPU. This provides much higher performance for floating point workloads compared to software emulation.
Enabling the FPU requires building with appropriate compiler flags and linking against libraries like newlib with FPU support enabled. The result can be 2-10X faster floating point computation.
ARMv7E-M architecture includes SIMD instructions optimized for multimedia. Intrinsics allow building NEON vector operations into code to significantly accelerate performance.
Common examples include image filters, audio codecs, computer vision algorithms. Vectorizing key loops and hot code paths can provide major speedups.
In addition to NEON, the Cortex-M4 incorporates DSP extensions for digital signal processing algorithms. This includes saturating arithmetic, rounding modes, and fast multiply-accumulates.
DSP intrinsics help optimize signal processing workflows for audio, speech, image, and video applications.
Attached coprocessors can offload specialized processing from the CPU. This helps accelerate workloads and reduce power consumption of the main processor.
Example coprocessors include cryptographic accelerators for encryption/decryption, image signal processors for computer vision, and math coprocessors for computations.
Managing Software Complexity
While cross-compiling gives many benefits, it also introduces complexities from the differences between the build and target platforms. Careful software design can help manage this complexity.
Hardware Abstraction Layers
A hardware abstraction layer (HAL) hides low-level hardware interactions from higher level software. This improves code portability across different target platforms.
Common uses include standardizing access to peripherals, I/O interfaces, and device drivers. A HAL allows software to be reused across an SoC family.
Board Support Packages
Board support packages (BSPs) include target-specific hardware definitions, drivers, libraries, and other glue code. The BSP abstracts board-level implementation details from application code.
BSPs allow application code to remain portable. The BSP is customized for each target board rather than changing the app code.
Carefully architecting software libraries also promotes reuse, maintainability and portability. Some design principles include:
- Loose coupling between modules
- Clear division of responsibilities
- Explicit published interfaces
- Information hiding
- Configuration versus compilation
Well designed libraries with clean interfaces and data hiding maximize code reuse while minimizing target-specific changes.
Reusing well-tested and optimized middleware components can accelerate embedded development. This avoids reinventing standard functionality.
Real-Time Operating Systems
A real-time operating system (RTOS) provides preemptive multitasking and scheduler to manage multiple threads. This can simplify complex applications.
Common examples include FreeRTOS, ThreadX, Micrium uC/OS and TI-RTOS. RTOSes require understanding scheduling, mutexes, inter-thread communication, etc.
Networking stacks implement standard communication protocols. For example, lwIP implements TCP/IP on embedded devices. Other common protocol suites include USB and Bluetooth.
Reusing proven protocol implementations reduces design time and results in more robust communication code.
Embedded filesystem libraries like FatFs and LittleFS provide filesystem access and file management optimized for small MCUs. This can eliminate writing raw flash drivers.
Filesystems help organize changing data like logs, configuration, sensor measurements, etc. But they require careful design for robustness and efficiency.
Reusing Proven Code
Leveraging proven open source projects can accelerate development and improve software quality:
- Bootstrap validation – Don’t write low level drivers unless absolutely necessary
- Prioritize reuse over reinvention – Use established high quality software when feasible
- Thoroughly vet code – Review licensing, compatibility, maintenance
- Copy intelligently – Don’t blindly reuse without proper encapsulation
Evaluating project maturity and compatibility with design constraints is important when reusing open source code. Combining pieces into a coherent architecture is also key.
Efficiently integrating reusable components requires managing third party libraries:
- Use package managers – Simplify adding, removing, and updating libs
- Namespace conflicts – Isolate to prevent collisions
- Licensing – Understand and comply with open source licenses
- Security – Monitor for vulnerabilities
- Legacy code – Cleanup and remove unused, outdated libraries
Good library hygiene prevents subtle issues caused by neglecting dependencies over time. Periodic audits help identify problem areas like licensing conflicts or vulnerable components.
Automating build verification through continuous integration helps catch issues early:
- Fast feedback loops – Detect problems at commit/merge time
- Regression testing – Automatically re-run tests
- Enforce policies – Coding standards, license compliance, etc
- Easy developer workflow – Commit often without worrying about breakage
CI improves software quality and collaboration across a team. But requires investment in build infrastructure and test practices.
While powerful, cross-compiling comes with some pitfalls to be aware of:
- Subtle bugs – Behavior differences between build and target environments
- Limited testing – Cannot fully test on host machine
- Toolchain differences – Compiler, linker, libraries must match target
- Endianness – Mixing big and little endian code
- Timing – Race conditions and threading bugs may not manifest on host
Careful test design and defensive programming techniques help surface issues early before they make it to production.
Cross-compiling enables streamlined embedded development workflows by leveraging fast x86 host machines. But it requires adapting existing skills and gaining new expertise working across architectures.
With attention to choosing the right tools, configuring the build environment, designing portable code, and rigorous testing, cross-compiling facilitates robust and efficient embedded development.