This document describes the process of setting up and running the Arm® Ethos™-U55 NPU Inference Runner. The inference runner is intended for quickly checking profiling results for any desired network, providing it has been processed by the Vela compiler.
A simple model is provided with the Inference Runner as an example, but it is expected that the user will replace this model with one they wish to profile, see Add custom model for more details.
The inference runner is intended for quickly checking profiling results for any desired network providing it has been processed by the Vela compiler.
The inference runner will populate all input tensors for the provided model with randomly generated data and an inference is then performed. Profiling results are then displayed in the console.
Use case code could be found in source/use_case/inference_runner directory.
See Prerequisites
In addition to the already specified build option in the main documentation, the Inference Runner use case adds:
inference_runner_MODEL_TFLITE_PATH - Path to the NN model file in TFLite format. Model will be processed and included into the application axf file. The default value points to one of the delivered set of models. Note that the parameters TARGET_PLATFORM and ETHOS_U55_ENABLED should be aligned with the chosen model, i.e.:
ETHOS_U55_ENABLED is set to On or 1, the NN model is assumed to be optimized. The model will naturally all back to the Arm® Cortex®-M CPU if an unoptimized model is supplied.ETHOS_U55_ENABLED is set to Off or 0, the NN model is assumed to be unoptimized. Supplying an optimized model in this case will result in a runtime error.inference_runner_ACTIVATION_BUF_SZ: The intermediate/activation buffer size reserved for the NN model. By default, it is set to 2MiB and should be enough for most models.
In order to build ONLY Inference Runner example application add to the cmake command line specified in Building -DUSE_CASE_BUILD=inferece_runner.
Note: This section describes the process for configuring the build for
MPS3: SSE-300for different target platform see Building section.
Create a build directory and navigate inside:
mkdir build_inference_runner && cd build_inference_runner
On Linux, execute the following command to build only Inference Runner application to run on the Ethos-U55 Fast Model 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 \ -DUSE_CASE_BUILD=inference_runner ..
For Windows, add -G "MinGW Makefiles":
cmake \ -G "MinGW Makefiles" \ -DTARGET_PLATFORM=mps3 \ -DTARGET_SUBSYSTEM=sse-300 \ -DCMAKE_TOOLCHAIN_FILE=./scripts/cmake/bare-metal-toolchain.cmake \ -DUSE_CASE_BUILD=inference_runner ..
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 \ -DUSE_CASE_BUILD=inference_runner ..
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 \ -DUSE_CASE_BUILD=inference_runner ..
Note: If building for different Ethos-U55 configurations, see Configuring build for different Arm Ethos-U55 configurations:
If the TensorFlow source tree is not in its default expected location, set the path using TENSORFLOW_SRC_PATH. Similarly, if the Ethos-U55 driver is not in the default location, ETHOS_U55_DRIVER_SRC_PATH can be used to configure the 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 \ -DUSE_CASE_BUILD=inference_runner ..
Also, CMSIS_SRC_PATH parameter can be used to override the CMSIS sources used for compilation used by TensorFlow by default. For example, to use the CMSIS sources fetched by the ethos-u helper script, we can use:
cmake \ -DTARGET_PLATFORM=mps3 \ -DTARGET_SUBSYSTEM=sse-300 \ -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake \ -DTENSORFLOW_SRC_PATH=../ethos-u/core_software/tensorflow \ -DETHOS_U55_DRIVER_SRC_PATH=../ethos-u/core_software/core_driver \ -DCMSIS_SRC_PATH=../ethos-u/core_software/cmsis \ -DUSE_CASE_BUILD=inference_runner ..
Note: If re-building with changed parameters values, it is highly advised to clean the build directory and re-run the CMake command.
If the CMake command succeeded, build the application as follows:
make -j4
For Windows, use mingw32-make.
Add VERBOSE=1 to see compilation and link details.
Results of the build will be placed under build/bin folder:
bin ├── ethos-u-inference_runner.axf ├── ethos-u-inference_runner.htm ├── ethos-u-inference_runner.map ├── images-inference_runner.txt └── sectors ├── kws │ └── ... └── img_class ├── dram.bin └── itcm.bin
Where:
ethos-u-inference_runner.axf: The built application binary for the Inference Runner use case.
ethos-u-inference_runner.map: Information from building the application (e.g. libraries used, what was optimized, location of objects)
ethos-u-inference_runner.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-inference_runner.txt: Tells the FPGA which memory regions to use for loading the binaries in sectors/** folder.
The application performs inference using the model pointed to by the CMake parameter inference_runner_MODEL_TFLITE_PATH.
Note: If you want to run the model using Ethos-U55, ensure your custom model has been run through the Vela compiler successfully before continuing. See Optimize model with Vela compiler.
Then, you must set inference_runner_MODEL_TFLITE_PATH to the location of the Vela processed model file.
An example:
cmake \ -Dinference_runner_MODEL_TFLITE_PATH=<path/to/custom_model_after_vela.tflite> \ -DTARGET_PLATFORM=mps3 \ -DTARGET_SUBSYSTEM=sse-300 \ -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake ..
For Windows, add -G "MinGW Makefiles" to the CMake command.
Note: Clean the build directory before re-running the CMake command.
The .tflite model file pointed to by inference_runner_MODEL_TFLITE_PATH 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 has been used:
-- User option inference_runner_MODEL_TFLITE_PATH is set to <path/to/custom_model_after_vela.tflite> ... -- Using <path/to/custom_model_after_vela.tflite> ++ Converting custom_model_after_vela.tflite to\ custom_model_after_vela.tflite.cc ...
After compiling, your custom model will have now replaced the default one in the application.
The FVP is available publicly from Arm Ecosystem FVP downloads.
For Ethos-U55 evaluation, please download the MPS3 version of the Arm® Corstone™-300 model that contains Ethos-U55 and Cortex-M55. The model is currently only supported on Linux based machines. To install the FVP:
Unpack the archive
Run the install script in the extracted package
./FVP_Corstone_SSE-300_Ethos-U55.sh
Once completed the building step, application binary ethos-u-infernce_runner.axf can be found in the build/bin folder. Assuming the install location of the FVP was set to ~/FVP_install_location, the simulation can be started by:
~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/mps3-sse-300/ethos-u-inference_runner.axf
A log output should appear on the terminal:
telnetterminal0: Listening for serial connection on port 5000 telnetterminal1: Listening for serial connection on port 5001 telnetterminal2: Listening for serial connection on port 5002 telnetterminal5: Listening for serial connection on port 5003
This will also launch a telnet window with the sample application's standard output and error log entries containing information about the pre-built application version, TensorFlow Lite Micro library version used, data type as well as the input and output tensor sizes of the model compiled into the executable binary.
After the application has started the inference starts immediately and it outputs the results on the telnet terminal.
The following example illustrates application output:
[INFO] Profile for Inference: Active NPU cycles: 26976 Idle NPU cycles: 196
After running an inference on randomly generated data, the output of the log shows the profiling results that for this inference:
Ethos-U55's PMU report:
26,976 active cycles: number of cycles that were used for computation
196 idle cycles: number of cycles for which the NPU was idle
For FPGA platforms, CPU cycle count can also be enabled. For FVP, however, CPU cycle counters should not be used as the CPU model is not cycle-approximate or cycle-accurate.