Building the ML embedded code sample applications from sources

Building the Code Samples application from sources

This section assumes the use of an x86 Linux build machine.

Build prerequisites

Before proceeding, please, make sure that the following prerequisites are fulfilled:

Arm Compiler version 6.14 or above is installed and available on the path.
Test the compiler by running:
```
armclang -v
```
```
Product: ARM Compiler 6.14 Professional
Component: ARM Compiler 6.14
```
Note: Add compiler to the path, if needed:
export PATH=/path/to/armclang/bin:$PATH
Compiler license is configured correctly
CMake version 3.15 or above is installed and available on the path. Test CMake by running:
```
cmake --version
```
```
cmake version 3.16.2
```
Note: Add cmake to the path, if needed:
export PATH=/path/to/cmake/bin:$PATH
Python 3.6 or above is installed. Test python version by running:
```
python3 --version
```
```
Python 3.6.8
```
Build system will create python virtual environment during the build process. Please make sure that python virtual environment module is installed:
```
python3 -m venv
```
Make
```
make --version
```
```
GNU Make 4.1

...
```
Note: Add it to the path environment variable, if needed.
Access to the Internet to download the third party dependencies, specifically: TensorFlow Lite Micro, Arm Ethos-U55 NPU driver and CMSIS. Instructions for downloading these are listed under preparing build environment.

Build options

The project build system allows user to specify custom NN model (in .tflite format) or images and compile application binary from sources.

The build system uses pre-built TensorFlow Lite for Microcontrollers library and Arm® Ethos™-U55 driver libraries from the delivery package.

The build script is parameterized to support different options. Default values for build parameters will build the executable compatible with the Ethos-U55 NPU Fast Model.

The build parameters are:

TARGET_PLATFORM: Target platform to execute application:
- mps3
- native
- simple_platform
TARGET_SUBSYSTEM: Platform target subsystem; this specifies the design implementation for the deployment target. For both, the MPS3 FVP and the MPS3 FPGA, this should be left to the default value of SSE-300:
- sse-300 (default - Arm® Corstone™-300)
- sse-200
TENSORFLOW_SRC_PATH: Path to the root of the TensorFlow directory. The default value points to the TensorFlow submodule in the ethos-u dependencies folder.
ETHOS_U55_DRIVER_SRC_PATH: Path to the Ethos-U55 NPU core driver sources. The default value points to the core_driver submodule in the ethos-u dependencies folder.
CMSIS_SRC_PATH: Path to the CMSIS sources to be used to build TensorFlow Lite Micro library. This parameters is optional and valid only for Arm® Cortex®-M CPU targeted configurations. The default value points to the CMSIS submodule in the ethos-u dependencies folder.
ETHOS_U55_ENABLED: Sets whether the use of Ethos-U55 NPU is available for the deployment target. By default, this is set and therefore application is built with Ethos-U55 NPU supported.
CPU_PROFILE_ENABLED: Sets whether profiling information for the CPU core should be displayed. By default, this is set to false, but can be turned on for FPGA targets. The the FVP, the CPU core's cycle counts are not meaningful and should not be used.
LOG_LEVEL: Sets the verbosity level for the application's output over UART/stdout. Valid values are LOG_LEVEL_TRACE, LOG_LEVEL_DEBUG, LOG_LEVEL_INFO, LOG_LEVEL_WARN and LOG_LEVEL_ERROR. By default, it is set to LOG_LEVEL_INFO.
<use_case>_MODEL_TFLITE_PATH: Path to the model file that will be processed and included into the application axf file. The default value points to one of the delivered set of models. Make sure the model chosen is aligned with the ETHOS_U55_ENABLED setting.
- When using Ethos-U55 NPU backend, the NN model is assumed to be optimized by Vela compiler. However, even if not, it will fall back on the CPU and execute, if supported by TensorFlow Lite Micro.
- When use of Ethos-U55 NPU is disabled, and if a Vela optimized model is provided, the application will report a failure at runtime.
USE_CASE_BUILD: specifies the list of applications to build. By default, the build system scans sources to identify available ML applications and produces executables for all detected use-cases. This parameter can accept single value, for example, USE_CASE_BUILD=img_class or multiple values, for example, USE_CASE_BUILD="img_class;kws".
ETHOS_U55_TIMING_ADAPTER_SRC_PATH: Path to timing adapter sources. The default value points to the timing_adapter dependencies folder.
TA_CONFIG_FILE: Path to the CMake configuration file containing the timing adapter parameters. Used only if the timing adapter build is enabled.
TENSORFLOW_LITE_MICRO_CLEAN_BUILD: Optional parameter to enable/disable "cleaning" prior to building for the TensorFlow Lite Micro library. It is enabled by default.
TENSORFLOW_LITE_MICRO_CLEAN_DOWNLOADS: Optional parameter to enable wiping out TPIP downloads from TensorFlow source tree prior to each build. It is disabled by default.
ARMCLANG_DEBUG_DWARF_LEVEL: When the CMake build type is specified as Debug and when armclang toolchain is being used to build for a Cortex-M CPU target, this optional argument can be set to specify the DWARF format. By default, this is set to 4 and is synonymous with passing -g flag to the compiler. This is compatible with Arm-DS and other tools which can interpret the latest DWARF format. To allow debugging using the Model Debugger from Arm FastModel Tools Suite, this argument can be used to pass DWARF format version as "3". Note: this option is only available when CMake project is configured with -DCMAKE_BUILD_TYPE=Debug argument. Also, the same dwarf format is used for building TensorFlow Lite Micro library.

Note: For details on the specific use case build options, follow the instructions in the use-case specific documentation. Also, when setting any of the CMake configuration parameters that expect a directory/file path , it is advised to use absolute paths instead of relative paths.

Build process

The build process can summarized in three major steps:

Prepare the build environment by downloading third party sources required, see Preparing build environment.
Configure the build for the platform chosen. This stage includes:
- CMake options configuration
- When <use_case>_MODEL_TFLITE_PATH build options aren't provided, defaults neural network models are be downloaded from Arm ML-Zoo. In case of native build, network's input and output data for tests are downloaded.
- Some files such as neural network models, network's inputs and output labels are automatically converted into C/C++ arrays, see Automatic file generation.
Build the application.
During this stage application and third party libraries are built see Building the configured project.

Preparing build environment

Certain third party sources are required to be present on the development machine to allow the example sources in this repository to link against.

These are part of the ethos-u repository and set as submodules of this project.

To pull the submodules:

git submodule update --init

This will download all the required components and place them in a tree like:

dependencies
 └── ethos-u
     ├── cmsis
     ├── core_driver
     ├── tensorflow
     └── ...

NOTE: The default source paths for the TPIP sources assume the above directory structure, but all of the relevant paths can be overridden by CMake configuration arguments TENSORFLOW_SRC_PATH, ETHOS_U55_DRIVER_SRC_PATH, and CMSIS_SRC_PATH.

Create a build directory

Create a build directory in the root of the project and navigate inside:

mkdir build && cd build

Configuring the build for MPS3: SSE-300

On Linux, execute the following command to build the application to run on the Ethos-U55 NPU when providing only the mandatory arguments for CMake configuration:

cmake \
    -DTARGET_PLATFORM=mps3 \
    -DTARGET_SUBSYSTEM=sse-300 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake ..

Toolchain option CMAKE_TOOLCHAIN_FILE points to the toolchain specific file to set the compiler and platform specific parameters.

To configure a build that can be debugged using Arm-DS, we can just specify the build type as Debug:

cmake \
    -DTARGET_PLATFORM=mps3 \
    -DTARGET_SUBSYSTEM=sse-300 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake \
    -DCMAKE_BUILD_TYPE=Debug ..

To configure a build that can be debugged using a tool that only supports DWARF format 3 (Modeldebugger for example), we can use:

cmake \
    -DTARGET_PLATFORM=mps3 \
    -DTARGET_SUBSYSTEM=sse-300 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake \
    -DCMAKE_BUILD_TYPE=Debug \
    -DARMCLANG_DEBUG_DWARF_LEVEL=3 ..

If the TensorFlow source tree is not in its default expected location, set the path using TENSORFLOW_SRC_PATH. Similarly, if the Ethos-U55 NPU driver and CMSIS are not in the default location, ETHOS_U55_DRIVER_SRC_PATH and CMSIS_SRC_PATH can be used to configure their location. For example:

cmake \
    -DTARGET_PLATFORM=mps3 \
    -DTARGET_SUBSYSTEM=sse-300 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake \
    -DTENSORFLOW_SRC_PATH=/my/custom/location/tensorflow \
    -DETHOS_U55_DRIVER_SRC_PATH=/my/custom/location/core_driver \
    -DCMSIS_SRC_PATH=/my/custom/location/cmsis ..

Note: If re-building with changed parameters values, it is highly advised to clean the build directory and re-run the CMake command.

Configuring the build for MPS3: SSE-200

cmake \
    -DTARGET_PLATFORM=mps3 \
    -DTARGET_SUBSYSTEM=sse-200 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake ..

for Windows add -G "MinGW Makefiles":

cmake \
    -DTARGET_PLATFORM=mps3 \
    -DTARGET_SUBSYSTEM=sse-200 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake \
    -G "MinGW Makefiles ..

Configuring native unit-test build

cmake \
    -DTARGET_PLATFORM=native \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/native-toolchain.cmake ..

Results of the build will be placed under build/bin/ folder:

 bin
  |- dev_ethosu_eval-tests
  |_ ethos-u

Configuring the build for simple_platform

cmake \
    -DTARGET_PLATFORM=simple_platform \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake ..

For Windows add -G "MinGW Makefiles":

cmake \
    -DTARGET_PLATFORM=simple_platform \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake \
    -G "MinGW Makefiles" ..

Building the configured project

If the CMake command succeeds, build the application as follows:

make -j4

Add VERBOSE=1 to see compilation and link details.

Results of the build will be placed under build/bin folder, an example:

bin
 ├── ethos-u-<use_case_name>.axf
 ├── ethos-u-<use_case_name>.htm
 ├── ethos-u-<use_case_name>.map
 ├── images-<use_case_name>.txt
 └── sectors
        └── <use_case>
                ├── dram.bin
                └── itcm.bin

Where for each implemented use-case under the source/use-case directory, the following build artefacts will be created:

ethos-u-<use case name>.axf: The built application binary for a ML use case.
ethos-u-<use case name>.map: Information from building the application (e.g. libraries used, what was optimized, location of objects).
ethos-u-<use case name>.htm: Human readable file containing the call graph of application functions.
sectors/: Folder containing the built application, split into files for loading into different FPGA memory regions.
images-<use case name>.txt: Tells the FPGA which memory regions to use for loading the binaries in sectors/** folder.

Note: For the specific use case commands see the relative section in the use case documentation.

Building timing adapter with custom options

The sources also contains the configuration for a timing adapter utility for the Ethos-U55 NPU driver. The timing adapter allows the platform to simulate user provided memory bandwidth and latency constraints.

The timing adapter driver aims to control the behavior of two AXI buses used by Ethos-U55 NPU. One is for SRAM memory region and the other is for flash or DRAM. The SRAM is where intermediate buffers are expected to be allocated and therefore, this region can serve frequent R/W traffic generated by computation operations while executing a neural network inference. The flash or DDR is where we expect to store the model weights and therefore, this bus would typically be used only for R/O traffic.

It is used for MPS3 FPGA as well as for Fast Model environment.

The CMake build framework allows the parameters to control the behavior of each bus with following parameters:

MAXR: Maximum number of pending read operations allowed. 0 is inferred as infinite, and the default value is 4.
MAXW: Maximum number of pending write operations allowed. 0 is inferred as infinite, and the default value is 4.
MAXRW: Maximum number of pending read+write operations allowed. 0 is inferred as infinite, and the default value is 8.
RLATENCY: Minimum latency, in cycle counts, for a read operation. This is the duration between ARVALID and RVALID signals. The default value is 50.
WLATENCY: Minimum latency, in cycle counts, for a write operation. This is the duration between WVALID + WLAST and BVALID being de-asserted. The default value is 50.
PULSE_ON: Number of cycles during which addresses are let through. The default value is 5100.
PULSE_OFF: Number of cycles during which addresses are blocked. The default value is 5100.
BWCAP: Maximum number of 64-bit words transferred per pulse cycle. A pulse cycle is PULSE_ON + PULSE_OFF. 0 is inferred as infinite, and the default value is 625.
MODE: Timing adapter operation mode. Default value is 0
- Bit 0: 0=simple; 1=latency-deadline QoS throttling of read vs. write
- Bit 1: 1=enable random AR reordering (0=default),
- Bit 2: 1=enable random R reordering (0=default),
- Bit 3: 1=enable random B reordering (0=default)

For timing adapter's CMake build configuration, the SRAM AXI is assigned index 0 and the flash/DRAM AXI bus has index 1. To change the bus parameter for the build a "TA_<index>_" prefix should be added to the above. For example, TA0_MAXR=10 will set the SRAM AXI bus's maximum pending reads to 10.

As an example, if we have the following parameters for flash/DRAM region:

TA1_MAXR = "2"
TA1_MAXW = "0"
TA1_MAXRW = "0"
TA1_RLATENCY = "64"
TA1_WLATENCY = "32"
TA1_PULSE_ON = "320"
TA1_PULSE_OFF = "80"
TA1_BWCAP = "50"

For a clock rate of 500MHz, this would translate to:

The maximum duty cycle for any operation is:
Maximum bit rate for this bus (64-bit wide) is:
With a read latency of 64 cycles, and maximum pending reads as 2, each read could be a maximum of 64 or 128 bytes, as defined for Ethos-U55 NPU's AXI bus's attribute.
The bandwidth is calculated solely by read parameters
This is higher than the overall bandwidth dictated by the bus parameters of

This suggests that the read operation is limited only by the overall bus bandwidth.

Timing adapter requires recompilation to change parameters. Default timing adapter configuration file pointed to by TA_CONFIG_FILE build parameter is located in the scripts/cmake folder and contains all options for AXI0 and AXI1 described above.

An example of scripts/cmake/ta_config.cmake:

# Timing adapter options
set(TA_INTERACTIVE OFF)

# Timing adapter settings for AXI0
set(TA0_MAXR "8")
set(TA0_MAXW "8")
set(TA0_MAXRW "0")
set(TA0_RLATENCY "32")
set(TA0_WLATENCY "32")
set(TA0_PULSE_ON "3999")
set(TA0_PULSE_OFF "1")
set(TA0_BWCAP "4000")
...

An example of the build with custom timing adapter configuration:

cmake \
    -DTARGET_PLATFORM=mps3 \
    -DTARGET_SUBSYSTEM=sse-300 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake \
    -DTA_CONFIG_FILE=scripts/cmake/my_ta_config.cmake ..

Add custom inputs

The application performs inference on input data found in the folder set by the CMake parameters, for more information see the 3.3 section in the specific use case documentation.

Add custom model

The application performs inference using the model pointed to by the CMake parameter MODEL_TFLITE_PATH.

Note: If you want to run the model using Ethos-U55 NPU, ensure your custom model has been run through the Vela compiler successfully before continuing.

To run the application with a custom model you will need to provide a labels_<model_name>.txt file of labels associated with the model. Each line of the file should correspond to one of the outputs in your model. See the provided labels_mobilenet_v2_1.0_224.txt file in the img_class use case for an example.

Then, you must set <use_case>_MODEL_TFLITE_PATH to the location of the Vela processed model file and <use_case>_LABELS_TXT_FILE to the location of the associated labels file:

cmake \
    -D<use_case>_MODEL_TFLITE_PATH=<path/to/custom_model_after_vela.tflite> \
    -D<use_case>_LABELS_TXT_FILE=<path/to/labels_custom_model.txt> \
    -DTARGET_PLATFORM=mps3 \
    -DTARGET_SUBSYSTEM=sse-300 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake ..

Note: For the specific use case command see the relative section in the use case documentation.

For Windows, add -G MinGW Makefiles to the CMake command.

Note: Clean the build directory before re-running the CMake command.

The TensorFlow Lite for Microcontrollers model pointed to by <use_case>_MODEL_TFLITE_PATH and labels text file pointed to by <use_case>_LABELS_TXT_FILE will be converted to C++ files during the CMake configuration stage and then compiled into the application for performing inference with.

The log from the configuration stage should tell you what model path and labels file have been used:

-- User option TARGET_PLATFORM is set to mps3
-- User option <use_case>_MODEL_TFLITE_PATH is set to
<path/to/custom_model_after_vela.tflite>
...
-- User option <use_case>_LABELS_TXT_FILE is set to
<path/to/labels_custom_model.txt>
...
-- Using <path/to/custom_model_after_vela.tflite>
++ Converting custom_model_after_vela.tflite to custom_model_after_vela.tflite.cc
-- Generating labels file from <path/to/labels_custom_model.txt>
-- writing to <path/to/build>/generated/include/Labels.hpp and <path/to/build>/generated/src/Labels.cc
...

After compiling, your custom model will have now replaced the default one in the application.

Optimize custom model with Vela compiler

Note: This tool is not available within this project. It is a python tool available from https://pypi.org/project/ethos-u-vela/. The source code is hosted on https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/.

The Vela compiler is a tool that can optimize a neural network model into a version that can run on an embedded system containing Ethos-U55 NPU.

The optimized model will contain custom operators for sub-graphs of the model that can be accelerated by Ethos-U55 NPU, the remaining layers that cannot be accelerated are left unchanged and will run on the CPU using optimized (CMSIS-NN) or reference kernels provided by the inference engine.

After the compilation, the optimized model can only be executed on a system with Ethos-U55 NPU.

Note: The NN model provided during the build and compiled into the application executable binary defines whether CPU or NPU is used to execute workloads. If unoptimized model is used, then inference will run on Cortex-M CPU.

Vela compiler accepts parameters to influence a model optimization. The model provided within this project has been optimized with the following parameters:

vela \
    --accelerator-config=ethos-u55-128 \
    --block-config-limit=0 \
    --config my_vela_cfg.ini \
    --memory-mode Shared_Sram \
    --system-config Ethos_U55_High_End_Embedded \
    <model>.tflite

Where:

--accelerator-config: Specify the accelerator configuration to use between ethos-u55-256, ethos-u55-128, ethos-u55-64 and ethos-u55-32.
--block-config-limit: Limit block config search space, use zero for unlimited.
--config: Specifies the path to the Vela configuration file. The format of the file is a Python ConfigParser .ini file. An example can be found in the dependencies folder vela.ini.
--memory-mode: Selects the memory mode to use as specified in the Vela configuration file.
--system-config:Selects the system configuration to use as specified in the Vela configuration file.

Vela compiler accepts .tflite file as input and saves optimized network model as a .tflite file.

Using --show-cpu-operations and --show-subgraph-io-summary will show all the operations that fall back to the CPU and a summary of all the subgraphs and their inputs and outputs.

To see Vela helper for all the parameters use: vela --help.

Please, get in touch with your Arm representative to request access to Vela Compiler documentation for more details.

Note: By default, use of the Ethos-U55 NPU is enabled in the CMake configuration. This could be changed by passing -DETHOS_U55_ENABLED.

Memory constraints

Both the MPS3 Fixed Virtual Platform and the MPS3 FPGA platform share the linker script (scatter file) for SSE-300 design. The design is set by the CMake configuration parameter TARGET_SUBSYSTEM as described in the previuous section.

The memory map exposed by this design is presented in Appendix 1. This can be used as a reference when editing the scatter file, especially to make sure that region boundaries are respected. The snippet from MPS3's scatter file is presented below:

;---------------------------------------------------------
; First load region
;---------------------------------------------------------
LOAD_REGION_0 0x00000000 0x00080000
{
    ;-----------------------------------------------------
    ; First part of code mem -- 512kiB
    ;-----------------------------------------------------
    itcm.bin 0x00000000 0x00080000
    {
        *.o (RESET, +First)
        * (InRoot$$Sections)
        .ANY (+RO)
    }

    ;-----------------------------------------------------
    ; 128kiB of 512kiB bank is used for any other RW or ZI
    ; data. Note: this region is internal to the Cortex-M CPU
    ;-----------------------------------------------------
    dtcm.bin 0x20000000 0x00020000
    {
        .ANY(+RW +ZI)
    }

    ;-----------------------------------------------------
    ; 128kiB of stack space within the DTCM region
    ;-----------------------------------------------------
    ARM_LIB_STACK 0x20020000 EMPTY ALIGN 8 0x00020000
    {}

    ;-----------------------------------------------------
    ; 256kiB of heap space within the DTCM region
    ;-----------------------------------------------------

    ARM_LIB_HEAP 0x20040000 EMPTY ALIGN 8 0x00040000
    {}

    ;-----------------------------------------------------
    ; SSE-300's internal SRAM
    ;-----------------------------------------------------
    isram.bin 0x21000000 UNINIT ALIGN 16 0x00080000
    {
        ; activation buffers a.k.a tensor arena
        *.o (.bss.NoInit.activation_buf)
    }
}

;---------------------------------------------------------
; Second load region
;---------------------------------------------------------
LOAD_REGION_1 0x60000000 0x02000000
{
    ;-----------------------------------------------------
    ; 32 MiB of DRAM space for nn model and input vectors
    ;-----------------------------------------------------
    dram.bin 0x60000000 ALIGN 16 0x02000000
    {
        ; nn model's baked in input matrices
        *.o (ifm)

        ; nn model
        *.o (nn_model)

        ; if the activation buffer (tensor arena) doesn't
        ; fit in the SRAM region, we accommodate it here
        *.o (activation_buf)
    }
}

It is worth noting that in the bitfile implementation, only the BRAM, internal SRAM and DDR memory regions are accessible to the Ethos-U55 NPU block. In the above snippet, the internal SRAM region memory can be seen to be utilized by activation buffers with a limit of 512kiB. If used, this region will be written to by the Ethos-U55 NPU block frequently. A bigger region of memory for storing the model is placed in the DDR region, under LOAD_REGION_1. The two load regions are necessary as the MPS3's motherboard configuration controller limits the load size at address 0x00000000 to 512kiB. This has implications on how the application is deployed on MPS3 as explained under the section 3.8.3.

Automatic file generation

As mentioned in the previous sections, some files such as neural network models, network's inputs, and output labels are automatically converted into C/C++ arrays during the CMake project configuration stage. Additionally, some code is generated to allow access to these arrays.

An example:

-- Building use-cases: img_class.
-- Found sources for use-case img_class
-- User option img_class_FILE_PATH is set to /tmp/samples
-- User option img_class_IMAGE_SIZE is set to 224
-- User option img_class_LABELS_TXT_FILE is set to /tmp/labels/labels_model.txt
-- Generating image files from /tmp/samples
++ Converting cat.bmp to cat.cc
++ Converting dog.bmp to dog.cc
-- Skipping file /tmp/samples/files.md due to unsupported image format.
++ Converting kimono.bmp to kimono.cc
++ Converting tiger.bmp to tiger.cc
++ Generating /tmp/build/generated/img_class/include/InputFiles.hpp
-- Generating labels file from /tmp/labels/labels_model.txt
-- writing to /tmp/build/generated/img_class/include/Labels.hpp and /tmp/build/generated/img_class/src/Labels.cc
-- User option img_class_ACTIVATION_BUF_SZ is set to 0x00200000
-- User option img_class_MODEL_TFLITE_PATH is set to /tmp/models/model.tflite
-- Using /tmp/models/model.tflite
++ Converting model.tflite to    model.tflite.cc
...

In particular, the building options pointing to the input files <use_case>_FILE_PATH, the model <use_case>_MODEL_TFLITE_PATH and labels text file <use_case>_LABELS_TXT_FILE are used by python scripts in order to generate not only the converted array files, but also some headers with utility functions.

For example, the generated utility functions for image classification are:

build/generated/include/InputFiles.hpp

#ifndef GENERATED_IMAGES_H
#define GENERATED_IMAGES_H

#include <cstdint>

#define NUMBER_OF_FILES  (2U)
#define IMAGE_DATA_SIZE  (150528U)

extern const uint8_t im0[IMAGE_DATA_SIZE];
extern const uint8_t im1[IMAGE_DATA_SIZE];

const char* get_filename(const uint32_t idx);
const uint8_t* get_img_array(const uint32_t idx);

#endif /* GENERATED_IMAGES_H */

build/generated/src/InputFiles.cc

#include "InputFiles.hpp"

static const char *img_filenames[] = {
    "img1.bmp",
    "img2.bmp",
};

static const uint8_t *img_arrays[] = {
    im0,
    im1
};

const char* get_filename(const uint32_t idx)
{
    if (idx < NUMBER_OF_FILES) {
        return img_filenames[idx];
    }
    return nullptr;
}

const uint8_t* get_img_array(const uint32_t idx)
{
    if (idx < NUMBER_OF_FILES) {
        return img_arrays[idx];
    }
    return nullptr;
}

These headers are generated using python templates, that are in scripts/py/templates/*.template.

scripts/
├── cmake
│   ├── ...
│   ├── subsystem-profiles
│   │   ├── corstone-sse-200.cmake
│   │   └── corstone-sse-300.cmake
│   ├── templates
│   │   ├── mem_regions.h.template
│   │   ├── peripheral_irqs.h.template
│   │   └── peripheral_memmap.h.template
│   └── ...
└── py
    ├── <generation scripts>
    ├── requirements.txt
    └── templates
        ├── audio.cc.template
        ├── AudioClips.cc.template
        ├── AudioClips.hpp.template
        ├── default.hpp.template
        ├── header_template.txt
        ├── image.cc.template
        ├── Images.cc.template
        ├── Images.hpp.template
        ├── Labels.cc.template
        ├── Labels.hpp.template
        ├── testdata.cc.template
        ├── TestData.cc.template
        ├── TestData.hpp.template
        └── tflite.cc.template

Based on the type of use case the correct conversion is called in the use case cmake file (audio or image respectively for voice or vision use cases). For example, the generations call for image classification (source/use_case/img_class/usecase.cmake):

# Generate input files
generate_images_code("${${use_case}_FILE_PATH}"
                     ${SRC_GEN_DIR}
                     ${INC_GEN_DIR}
                     "${${use_case}_IMAGE_SIZE}")

# Generate labels file
set(${use_case}_LABELS_CPP_FILE Labels)
generate_labels_code(
    INPUT           "${${use_case}_LABELS_TXT_FILE}"
    DESTINATION_SRC ${SRC_GEN_DIR}
    DESTINATION_HDR ${INC_GEN_DIR}
    OUTPUT_FILENAME "${${use_case}_LABELS_CPP_FILE}"
)

...

# Generate model file
generate_tflite_code(
    MODEL_PATH ${${use_case}_MODEL_TFLITE_PATH}
    DESTINATION ${SRC_GEN_DIR}
)

Note: When required, for models and labels conversion it's possible to add extra parameters such as extra code to put in <model>.cc file or namespaces.

set(${use_case}_LABELS_CPP_FILE Labels)
generate_labels_code(
    INPUT           "${${use_case}_LABELS_TXT_FILE}"
    DESTINATION_SRC ${SRC_GEN_DIR}
    DESTINATION_HDR ${INC_GEN_DIR}
    OUTPUT_FILENAME "${${use_case}_LABELS_CPP_FILE}"
    NAMESPACE       "namespace1" "namespace2"
)

...

set(EXTRA_MODEL_CODE
    "/* Model parameters for ${use_case} */"
    "extern const int   g_myvariable2     = value1"
    "extern const int   g_myvariable2     = value2"
)

generate_tflite_code(
    MODEL_PATH ${${use_case}_MODEL_TFLITE_PATH}
    DESTINATION ${SRC_GEN_DIR}
    EXPRESSIONS ${EXTRA_MODEL_CODE}
    NAMESPACE   "namespace1" "namespace2"
)

In addition to input file conversions, the correct platform/system profile is selected (in scripts/cmake/subsystem-profiles/*.cmake) based on TARGET_SUBSYSTEM build option and the variables set are used to generate memory region sizes, base addresses and IRQ numbers, respectively used to generate mem_region.h, peripheral_irqs.h and peripheral_memmap.h headers. Templates from scripts/cmake/templates/*.template are used to generate the header files.

After the build, the files generated in the build folder are:

build/generated/
├── bsp
│   ├── mem_regions.h
│   ├── peripheral_irqs.h
│   └── peripheral_memmap.h
├── <use_case_name1>
│   ├── include
│   │   ├── InputFiles.hpp
│   │   └── Labels.hpp
│   └── src
│       ├── <uc1_input_file1>.cc
│       ├── <uc1_input_file2>.cc
│       ├── InputFiles.cc
│       ├── Labels.cc
│       └── <uc1_model_name>.tflite.cc
└──  <use_case_name2>
    ├── include
    │   ├── InputFiles.hpp
    │   └── Labels.hpp
    └── src
        ├── <uc2_input_file1>.cc
        ├── <uc2_input_file2>.cc
        ├── InputFiles.cc
        ├── Labels.cc
        └── <uc2_model_name>.tflite.cc

Next section of the documentation: Deployment.