You should use an x86_64 development machine running Windows or Linux for the best experience.
The Arm ML Evaluation Kit (MLEK) is not fully supported on Windows. Some of the required tools work only on Linux. Linux is recommended if you plan to use MLEK extensively.
There are some ML examples which can be developed using Windows tools.
The same development tools for general embedded projects are needed for ML applications, but there are additional tools and software which are also common in ML applications.
You can build ML applications with Arm Compiler for Embedded or Arm GNU Toolchain .
Use the install guides to install the compilers on your computer:
Both compilers are pre-installed in Arm Virtual Hardware.
Use the Arm Development Studio install guide to set up Arm DS on Linux or Windows.
Keil MDK is a popular microcontroller development toolkit on Windows.
Both IDEs contain Arm Compiler for Embedded to build applications, and can connect to the Ecosystem FVPs for software debug and test.
Arm Keil Studio Cloud also offers the ability to run software on the Corstone-300 FVP. A
list of software projects
is available for you to browse.
A number of other tools are common in ML applications.
You may want to use Docker to simplify ML development environment creation.
As an example, clone the MLEK repository and look at the Dockerfile at the top of the repository to see one way to use Docker in ML application development:
git clone "https://review.mlplatform.org/ml/ethos-u/ml-embedded-evaluation-kit"
cd ml-embedded-evaluation-kit
git submodule update --init
Use an editor or program such as more or cat to view the Dockerfile.
TensorFlow Lite for Microcontrollers (TFLM) is the most common framework for microcontroller ML applications.
TensorFlow uses
xxd
to convert TensorFlow Lite models into C data structures.
PyTorch is also used on microcontrollers.
Apache TVM is an open-source compiler framework for ML and can be used on microcontrollers.
The ML ecosystem is changing rapidly with many new companies and software. You can refer to the AI Ecosystem Catalog to find ML solutions for Cortex-M microcontrollers.
Vela is used to compile a TFLM neural network model into an optimized version that can run on hardware containing an Arm Ethos-U NPU.
Install it using pip3:
pip install ethos-u-vela
The CMSIS-NN software library is a collection of efficient neural network kernels developed to maximize the performance and minimize the memory footprint of neural networks on Arm Cortex-M processors.
The library is used by frameworks for operations that cannot be executed on the Ethos processor.
Accelerated inference on Arm microcontrollers with TensorFlow Lite for Microcontrollers and CMSIS-NN provides an excellent overview.
Resources for learning about ML applications are listed below for you to investigate and learn from.
The MLEK provides a number of example ML applications.
Build the ML Evaluation Kit examples is the fastest way to build and run the MLEK examples, and includes next steps to learn more.
The Micro speech example for TensorFlow Lite is a good way to get started learning ML applications.
Refer to the information included in the AVH documentation for more details.
You can run micro speech is just a few steps on the AVH AMI:
git clone https://github.com/ARM-software/AVH-TFLmicrospeech.git
cd AVH-TFLmicrospeech/Platform_FVP_Corstone_SSE-300_Ethos-U55
cbuild.sh --packs microspeech.Example.cprj
./run_example.sh
The micro speech application will print the Heard yes and Heard no ML inferences from the audio samples, similar to:
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
Ethos-U rev 136b7d75 --- Feb 16 2022 15:47:15
(C) COPYRIGHT 2019-2022 Arm Limited
ALL RIGHTS RESERVED
Heard yes (146) @1000ms
Heard no (145) @5600ms
Heard yes (143) @9100ms
Heard no (145) @13600ms
...
The Open-IoT-SDK includes a number of ML applications and demonstrates concepts such as how to integrate Arm Trusted Firmware for Cortex-M with an ML application.
Refer to Build and run Open-IoT-SDK examples to learn how to use the SDK.
To learn about TVM, refer to Running TVM on bare metal Cortex-M55 and Ethos-U55 NPU with CMSIS-NN .
You will learn the basics of TVM and more about CMSIS-NN , the software library of efficient neural network kernels for Cortex-M.
Additional examples are in an Arm GitHub repository called ML-examples
The potential use cases of machine learning are very broad. Please propose your favorite applications for Cortex-M and Ethos-U ML in GitHub discussions .