README.md
1# CMSIS NN
2CMSIS NN software library is a collection of efficient neural network kernels developed to maximize the
3performance and minimize the memory footprint of neural networks on Arm Cortex-M processors.
4
5## Supported Framework
6The library follows the [int8](https://www.tensorflow.org/lite/performance/quantization_spec) and int16 quantization specification of TensorFlow Lite for Microcontrollers.
7
8## Branches and Tags
9There is a single branch called 'main'.
10Tags are created during a release. Two releases are planned to be done in a year. The releases can be found
11[here](https://github.com/ARM-software/CMSIS-NN/releases) .
12
13## Current Operator Support
14In general optimizations are written for an architecture feature. This falls into one of the following categories.
15Based on feature flags for a processor or architecture provided to the compiler, the right implementation is picked.
16### Pure C
17 There is always a pure C implementation for an operator. This is used for processors like Arm Cortex-M0 or Cortex-M3.
18### DSP Extension
19Processors with DSP extension uses Single Instruction Multiple Data(SIMD) instructions for optimization. Examples of
20processors here are Cortex-M4 or a Cortex-M33 configured with optional DSP extension.
21
22### MVE Extension
23Processors with Arm Helium Technology use the Arm M-profile Vector Extension(MVE) instructions for optimization.
24Examples are Cortex-M55 or Cortex-M85 configured with MVE.
25
26
27| Operator | C <br> int8 | C<br>int16 | DSP<br>int8 | DSP<br>int16 | MVE<br>int8 | MVE<br>int16 |
28| --------------- | ----------- | ---------- | ----------- | ------------ | ----------- | ------------ |
29| Conv2D | Yes | Yes | Yes | Yes | Yes | Yes |
30| DepthwiseConv2D | Yes | Yes | Yes | Yes | Yes | Yes |
31| Fully Connected | Yes | Yes | Yes | Yes | Yes | Yes |
32| Add | Yes | Yes | Yes | Yes | Yes | Yes |
33| Mul | Yes | Yes | Yes | Yes | Yes | Yes |
34| MaxPooling | Yes | Yes | Yes | Yes | Yes | Yes |
35| AvgPooling | Yes | Yes | Yes | Yes | Yes | Yes |
36| Softmax | Yes | Yes | Yes | Yes | Yes | No |
37| LSTM | Yes | NA | Yes | NA | Yes | NA |
38| SVDF | Yes | No | Yes | No | Yes | No |
39
40
41## Contribution Guideline
42First, a thank you for the contribution. Here are some guidelines and good to know information to get started.
43
44### Coding Guideline
45By default, follow the style used in the file. You'll soon start noticing a pattern like
46* Variable and function names are lower case with an underscore separator.
47* Hungarian notation is not used. Well, almost.
48* If the variable names don't convey the action, then add comments.
49
50### New Files
51One function per file is followed in most places. In those cases, the file name must match the function name. Connect
52the function to an appropriate Doxygen group as well.
53
54### Doxygen
55Function prototypes must have a detailed comment header in Doxygen format. You can execute the doxygen document generation
56script in the Doxygen folder to check that no errors are introduced.
57
58### Unit Tests
59For any new features and bug fixes, new unit tests are needed. Improvements have to be verifed by unit tests. If you do
60not have the means to execute the tests, you can still make the PR and comment that you need help in completing/executing
61the unit tests.
62
63### Version & Date
64Each File has a version number and a date field that must be updated when making any change to that file. The versioning
65follows Semantic Versioning 2.0.0 format.
66
67## Building CMSIS-NN as a library
68It is recommended to use toolchain files from [Arm Ethos-U Core Platform](https://review.mlplatform.org/admin/repos/ml/ethos-u/ethos-u-core-platform) project. These are supporting TARGET_CPU, which is a required argument. Note that if not specifying TARGET_CPU, these toolchains will set some default. The format must be TARGET_CPU=cortex-mXX, see examples below.
69
70Here is an example:
71
72```
73cd </path/to/CMSIS_NN>
74mkdir build
75cd build
76cmake .. -DCMAKE_TOOLCHAIN_FILE=</path/to/ethos-u-core-platform>/cmake/toolchain/arm-none-eabi-gcc.cmake -DTARGET_CPU=cortex-m55
77make
78```
79
80Some more examples:
81
82```
83cmake .. -DCMAKE_TOOLCHAIN_FILE=</path/to/ethos-u-core-platform>/cmake/toolchain/armclang.cmake -DTARGET_CPU=cortex-m55
84cmake .. -DCMAKE_TOOLCHAIN_FILE=</path/to/ethos-u-core-platform>/cmake/toolchain/arm-none-eabi-gcc.cmake -DTARGET_CPU=cortex-m7
85cmake .. -DCMAKE_TOOLCHAIN_FILE=</path/to/ethos-u-core-platform>/cmake/toolchain/armclang.cmake -DTARGET_CPU=cortex-m3
86```
87
88### Compiler Options
89Default optimization level is set at Ofast. Please change according to project needs. Just bear in mind this can impact
90performance. With only optimization level -O0, *ARM_MATH_AUTOVECTORIZE* needs to be defined for processors with Helium
91Technology.
92
93The compiler option *'-fomit-frame-pointer'* is enabled by default at -O and higher. When no optimization level is specified,
94you may need to specify '-fomit-frame-pointer'.
95
96The compiler option *'-fno-builtin'* does not utilize optimized implementations of e.g. memcpy and memset, which are heavily used by CMSIS-NN. It can significantly downgrade performance. So this should be avoided. The compiler option *'-ffreestanding'* should also be avoided as it enables '-fno-builtin' implicitly.
97
98### Supported Compilers
99* CMSIS-NN is tested on Arm Compiler 6 and on Arm GNU Toolchain.
100* IAR compiler is not tested and there can be compilation and/or performance issues.
101* Compilation for Host is not supported out of the box. It should be possible to use the C implementation and compile for host with minor stubbing effort.
102
103## Inclusive Language
104This product confirms to Arm’s inclusive language policy and, to the best of our knowledge, does not contain any non-inclusive language. If you find something that concerns you, email terms@arm.com.
105
106## Support / Contact
107
108For any questions or to reach the CMSIS-NN team, please create a new issue in https://github.com/ARM-software/CMSIS-NN/issues
