/CMSIS-DSP

CMSIS-DSP embedded compute library for Cortex-M and Cortex-A

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CMSIS-DSP

GitHub release (latest by date including pre-releases) GitHub

About

CMSIS-DSP is an optimized compute library for embedded systems (DSP is in the name for legacy reasons).

It provides optimized compute kernels for Cortex-M and for Cortex-A.

Different variants are available according to the core and most of the functions are using a vectorized version when the Helium or Neon extension is available.

This repository contains the CMSIS-DSP library and several other projects:

  • Test framework for bare metal Cortex-M or Cortex-A
  • Examples for bare metal Cortex-M
  • ComputeGraph
  • PythonWrapper

You don't need any of the other projects to build and use CMSIS-DSP library. Building the other projects may require installation of other libraries (CMSIS), other tools (Arm Virtual Hardware) or CMSIS build tools.

CMSIS-DSP Kernels

Kernels provided by CMSIS-DSP (list not exhaustive):

  • Basic mathematics (real, complex, quaternion, linear algebra, fast math functions)
  • DSP (filtering)
  • Transforms (FFT, MFCC, DCT)
  • Statistics
  • Classical ML (Support Vector Machine, Distance functions for clustering ...)

Kernels are provided with several datatypes : f64, f32, f16, q31, q15, q7.

Python wrapper

A PythonWrapper is also available and can be installed with:

pip install cmsisdsp

With this wrapper you can design your algorithm in Python using an API as close as possible to the C API. The wrapper is compatible with NumPy. The wrapper is supporting fixed point arithmetic.

The goal is to make it easier to move from a design to a final implementation in C.

Compute Graph

CMSIS-DSP is also providing an experimental static scheduler for compute graph to describe streaming solutions:

  • You define your compute graph in Python
  • A static and deterministic schedule (computed by the Python script) is generated
  • The static schedule can be run on the device with very low overhead

The Python scripts for the static scheduler generator are part of the CMSIS-DSP Python wrapper.

The header files are part of the CMSIS-DSP pack (version 1.10.2 and above).

The audio streaming nodes on top of CMSIS-RTOS2 are not part of the CMSIS-DSP pack but can be found in the repository. They are demo quality only. They can only be used with Arm Virtual Hardware.

The Compute Graph is making it easier to implement a streaming solution : connecting different compute kernels each consuming and producing different amount of data.

Support / Contact

For any questions or to reach the CMSIS-DSP team, please create a new issue in https://github.com/ARM-software/CMSIS-DSP/issues

Building for speed

CMSIS-DSP is used when you need performance. As consequence CMSIS-DSP should be compiled with the options giving the best performance:

Options to use

  • -Ofast must be used for best performances.
  • When using Helium it is strongly advised to use -Ofast

When float are used, then the fpu should be selected to ensure that the compiler is not using a software float emulation.

When building with Helium support, it will be automatically detected by CMSIS-DSP. For Neon, it is not the case and you must enable the option -DARM_MATH_NEON for the C compilation. With cmake this option is controlled with -DNEON=ON.

  • -DLOOPUNROLL=ON can also be used when compiling with cmake
  • It corresponds to the C options -DARM_MATH_LOOPUNROLL

Compilers are doing unrolling. So this option may not be needed but it is highly dependent on the compiler. With some compilers, this option is needed to get better performances.

Speed of memory is important. If you can map the data and the constant tables used by CMSIS-DSP in DTCM memory then it is better. If you have a cache, enable it.

Options to avoid

  • -fno-builtin
  • -ffreestanding because it enables previous options

The library is doing some type punning to process word 32 from memory as a pair of q15 or a quadruple of q7. Those type manipulations are done through memcpy functions. Most compilers should be able to optimize out those function calls when the length to copy is small (4 bytes).

This optimization will not occur when -fno-builtin is used and it will have a very bad impact on the performances.

Some compiler may also require the use of option -munaligned-access to specify that unaligned accesses are used.

Half float support

f16 data type (half float) has been added to the library. It is useful only if your Cortex has some half float hardware acceleration (for instance with Helium extension). If you don't need f16, you should disable it since it may cause compilation problems. Just define -DDISABLEFLOAT16 when building.

How to build

You can build CMSIS-DSP with the open CMSIS-Pack, or cmake, or Makefile and it is also easy to build if you use any other build tool.

Building with MDK or Open CMSIS-Pack

The standard way to build is by using the CMSIS pack technology. CMSIS-DSP is available as a pack.

This pack technology is supported by some IDE like Keil MDK or Keil studio.

You can also use those packs using the Open CMSIS-Pack technology and from command line on any platform.

You should first install the tools from https://github.com/Open-CMSIS-Pack/devtools

You can get the CMSIS-Toolbox which is containing the package installer, cmsis build and cmsis project manager. Here is some documentation:

Once you have installed the tools, you'll need to download the pack index using the cpackget tool.

Then, you'll need to convert a solution file into .cprj. For instance, for the CMSIS-DSP Examples, you can go to:

Examples/cmsis_build

and then type

csolution convert -s examples.csolution_ac6.yml

This command processes the examples.csolution_ac6.yml describing how to build the examples for several platforms. It will generate lots of .cprj files that can be built with cbuild.

If you want to build the FFT example for the Corstone-300 virtual hardware platform, you could just do:

cbuild "fftbin.Release+VHT-Corstone-300.cprj"

How to build with Make

There is an example Makefile in Source.

In each source folder (like BasicMathFunctions), you'll see files with no _datatype suffix (like BasicMathFunctions.c and BasicMathFunctionsF16.c).

Those files are all you need in your makefile. They are including all other C files from the source folders.

Then, for the includes you'll need to add the paths: Include, PrivateInclude and, since there is a dependency to CMSIS Core, Core/Include from CMSIS_5/CMSIS.

If you are building for Cortex-A and want to use Neon, you'll also need to include ComputeLibrary/Include and the source file in ComputeLibrary/Source.

How to build CMSIS-DSP with cmake

Create a CMakeLists.txt and inside add a project.

Add CMSIS-DSP as a subdirectory. The variable CMSISDSP is the path to the CMSIS-DSP repository in below example.

cmake_minimum_required (VERSION 3.14)

# Define the project
project (testcmsisdsp VERSION 0.1)

add_subdirectory(${CMSISDSP}/Source bin_dsp)

CMSIS-DSP is dependent on the CMSIS Core includes. So, you should define CMSISCORE on the cmake command line. The path used by CMSIS-DSP will be ${CMSISCORE}/Include.

You should also set the compilation options to use to build the library.

Launching the build

Once cmake has generated the makefiles, you can use a GNU Make to build.

make VERBOSE=1

How to build with any other build system

You need the following folders:

  • Source
  • Include
  • PrivateInclude
  • ComputeLibrary (only if you target Neon)

In Source subfolders, you may either build all of the source file with a datatype suffix (like _f32.c), or just compile the files without a datatype suffix. For instance for BasicMathFunctions, you can build all the C files except BasicMathFunctions.c and BasicMathFunctionsF16.c, or you can just build those two files (they are including all of the other C files of the folder).

f16 files are not mandatory. You can build with -DDISABLEFLOAT16

How to build for aarch64

The intrinsics defined in Core_A/Include are not available on recent Cortex-A processors.

But you can still build for those Cortex-A cores and benefit from the Neon intrinsics.

You need to build with -D__GNUC_PYTHON__ on the compiler command line. This flag was introduced for building the Python wrapper and is disabling the use of CMSIS Core includes.

When this flag is enabled, CMSIS-DSP is defining a few macros used in the library for compiler portability:

#define  __ALIGNED(x) __attribute__((aligned(x)))
#define __STATIC_FORCEINLINE static inline __attribute__((always_inline)) 
#define __STATIC_INLINE static inline

If the compiler you are using is requiring different definitions, you can add them to arm_math_types.h in the Include folder of the library. MSVC and XCode are already supported and in those case, you don't need to define -D__GNUC_PYTHON__

Then, you need to define -DARM_MATH_NEON

For cmake the equivalent options are:

  • -DHOST=ON
  • -DNEON=ON

cmake is automatically including the ComputeLibrary folder. If you are using a different build, you need to include this folder too to build with Neon support.

Running the examples

If you build the examples with CMSIS build tools, the generated executable can be run on a fast model. For instance, if you built for m7, you could just do:

FVP_MPS2_Cortex-M7.exe -a arm_variance_example

The final executable has no extension in the filename.

Of course, on your fast model or virtual hardware you should use the right configuration file (to enable float, to enable FVP, to enable semihosting if needed for the examples ...)

Folders and files

The only folders required to build and use CMSIS-DSP Library are:

  • Source
  • Include
  • PrivateInclude
  • ComputeLibrary (only when using Neon)

Other folders are part of different projects, tests or examples.

Folders

  • cmsisdsp

    • Required to build the CMSIS-DSP PythonWrapper for the Python repository
    • It contains all Python packages

  • ComputeLibrary:

    • Some kernels required when building CMSIS-DSP with Neon acceleration

  • Examples:

    • Examples of use of CMSIS-DSP on bare metal Cortex-M
    • Require the use of CMSIS Build tools

  • Include:

    • Include files for CMSIS-DSP

  • PrivateInclude:

    • Some include needed to build CMSIS-DSP

  • PythonWrapper:

    • C code for the CMSIS-DSP PythonWrapper
    • Examples for the PythonWrapper

  • Scripts:

    • Debugging scripts
    • Script to generate some coefficient tables used by CMSIS-DSP

  • Compute Graph:

    • Not needed to build CMSIS-DSP. This project is relying on CMSIS-DSP library
    • Examples for the Compute Graph
    • C++ templates for the Compute Graph
    • Default (and optional) nodes

  • Source:

    • CMSIS-DSP source

  • Testing:

    • CMSIS-DSP Test framework for bare metal Cortex-M and Cortex-A
    • Require the use of CMSIS build tools

Files

Some files are needed to generate the PythonWrapper:

  • PythonWrapper_README.md
  • LICENSE.txt
  • MANIFEST.in
  • pyproject.toml
  • setup.py

And we have a script to make it easier to customize the build:

  • cmsisdspconfig.py:
    • Web browser UI to generate build configurations (temporary until the CMSIS-DSP configuration is reworked to be simpler and more maintainable)

Compilation symbols for tables

Some new compilations symbols have been introduced to avoid including all the tables if they are not needed.

If no new symbol is defined, everything will behave as usual. If ARM_DSP_CONFIG_TABLES is defined then the new symbols will be taken into account.

It is strongly suggested to use the new Python script cmsisdspconfig.py to generate the -D options to use on the compiler command line.

pip install streamlit
streamlit run cmsisdspconfig.py

If you use cmake, it is also easy since high level options are defined and they will select the right compilation symbols.

For instance, if you want to use the arm_rfft_fast_f32, in fft.cmake you'll see an option RFFT_FAST_F32_32.

If you don't use cmake nor the Python script, you can just look at fft.cmake or interpol.cmake in Source to see which compilation symbols are needed.

We see, for arm_rfft_fast_f32, that the following symbols need to be enabled :

  • ARM_TABLE_TWIDDLECOEF_F32_16
  • ARM_TABLE_BITREVIDX_FLT_16
  • ARM_TABLE_TWIDDLECOEF_RFFT_F32_32
  • ARM_TABLE_TWIDDLECOEF_F32_16

In addition to that, ARM_DSP_CONFIG_TABLES must be enabled and finally ARM_FFT_ALLOW_TABLES must also be defined.

This last symbol is required because if no transform functions are included in the build, then by default all flags related to FFT tables are ignored.

Bit Reverse Tables for FFTs in CMSIS DSP

It is a question coming often.

It is now detailed in this github issue

Someone from the community has written a Python script to help