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+# Arm® ML embedded evaluation kit
+
+This repository is for building and deploying Machine Learning (ML) applications targeted for Arm® Cortex®-M and Arm®
+Ethos™-U NPU.
+To run evaluations using this software, we suggest using an [MPS3 board](https://developer.arm.com/tools-and-software/development-boards/fpga-prototyping-boards/mps3)
+or a fixed virtual platform (FVP) that supports Ethos-U55 software fast model. Both environments run a combination of
+the new [Arm® Cortex™-M55 processor](https://www.arm.com/products/silicon-ip-cpu/cortex-m/cortex-m55) and the
+[Arm® Ethos™-U55 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55).
+
+## Overview of the evaluation kit
+
+The purpose of the evaluation kit is to allow the user to develop software and test the performance of the Ethos-U55 NPU and
+Cortex-M55 CPU. The Ethos-U55 NPU is a new class of machine learning(ML) processor, specifically designed to accelerate
+computation for ML workloads in constrained embedded and IoT devices. The product is optimized to execute
+mathematical operations efficiently that are commonly used in ML algorithms, such as convolutions or activation functions.
+
+## ML use cases
+
+The evaluation kit adds value by providing ready to use ML applications for the embedded stack. As a result, you can
+experiment with the already developed software use cases and create your own applications for Cortex-M CPU and Ethos-U NPU.
+The example application at your disposal and the utilized models are listed in the table below.
+
+|   ML application                     |  Description | Neural Network Model |
+| :----------------------------------: | :-----------------------------------------------------: | :----: |
+|  [Image classification](./docs/use_cases/img_class.md)              | Recognize the presence of objects in a given image | [Mobilenet V2](https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8)   |
+|  [Keyword spotting(KWS)](./docs/use_cases/kws.md)             | Recognize the presence of a key word in a recording | [DS-CNN-L](https://github.com/ARM-software/ML-zoo/blob/master/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8) |
+|  [Automated Speech Recognition(ASR)](./docs/use_cases/asr.md) | Transcribe words in a recording | [Wav2Letter](https://github.com/ARM-software/ML-zoo/blob/master/models/speech_recognition/wav2letter/tflite_int8) |
+|  [KWS and ASR](./docs/use_cases/kws_asr.md) | Utilise Cortex-M and Ethos-U to transcribe words in a recording after a keyword was spotted | [DS-CNN-L](https://github.com/ARM-software/ML-zoo/blob/master/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8)  [Wav2Letter](https://github.com/ARM-software/ML-zoo/blob/master/models/speech_recognition/wav2letter/tflite_int8) |
+|  [Anomaly Detection](./docs/use_cases/ad.md)                 | Detecting abnormal behavior based on a sound recording of a machine | Coming soon|
+| [Generic inference runner](./docs/use_cases/inference_runner.md) | Code block allowing you to develop your own use case for Ethos-U55 NPU | Your custom model |
+
+The above use cases implement end-to-end ML flow including data pre-processing and post-processing. They will allow you
+to investigate embedded software stack, evaluate performance of the networks running on Cortex-M55 CPU and Ethos-U55 NPU
+by displaying different performance metrics such as inference cycle count estimation and results of the network execution.
+
+## Software and hardware overview
+
+The evaluation kit is based on the [Arm® Corstone™-300 reference package](https://developer.arm.com/ip-products/subsystem/corstone/corstone-300).
+Arm® Corstone™-300 helps you build SoCs quickly on the Arm® Cortex™-M55 and Arm® Ethos™-U55 designs. Arm® Corstone™-300 design
+implementation is publicly available on an [Arm MPS3 FPGA board](https://developer.arm.com/tools-and-software/development-boards/fpga-prototyping-boards/download-fpga-images),
+or as a [Fixed Virtual Platform of the MPS3 development board](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps).
+
+The Ethos-U NPU software stack is described [here](https://developer.arm.com/documentation/101888/0500/NPU-software-overview/NPU-software-components?lang=en).
+
+All ML use cases, albeit illustrating a different application, have common code such as initializing the Hardware
+Abstraction Layer (HAL). The application common code can be run on x86 or Arm Cortex-M architecture thanks to the HAL.
+For the ML application-specific part, Google® TensorFlow™ Lite for Microcontrollers inference engine is used to schedule
+the neural networks models executions. TensorFlow Lite for Microcontrollers is integrated with the [Ethos-U55 driver](https://git.mlplatform.org/ml/ethos-u/ethos-u-core-driver.git)
+and delegates execution of certain operators to the NPU or, if the neural network model operators are not supported on
+NPU, to the CPU. [CMSIS-NN](https://github.com/ARM-software/CMSIS_5) is used to optimise CPU workload execution
+with int8 data type.
+Common ML application functions will help you to focus on implementing logic of your custom ML use case: you can modify
+only the use case code and leave all other components unchanged. Supplied build system will discover new ML application
+code and automatically include it into compilation flow.
+
+![APIs](./docs/media/APIs_description.png)
+
+To run an ML application on the Cortex-M and Ethos-U55 NPU, please, follow these steps:
+
+1. Setup your environment by installing [the required prerequisites](./docs/sections/building.md#Build-prerequisites).
+2. Generate an optimized neural network model for Ethos-U with a Vela compiler by following instructions [here](./docs/sections/building.md#Add-custom-model).
+3. [Configure the build system](./docs/sections/building.md#Build-process).
+4. [Compile the project](./docs/sections/building.md#Building-the-configured-project) with a `make` command.
+5. If using a FVP, [launch the desired application on the FVP](./docs/sections/deployment.md#Fixed-Virtual-Platform).
+If using the FPGA option, load the image on the FPGA and [launch the application](./docs/sections/deployment.md#MPS3-board).
+
+To get familiar with these steps, you can follow the [quick start guide](docs/quick_start.md).
+
+For more details:
+
+- [Arm Ethos-U55 NPU Code Samples](docs/documentation.md)
+  - [Trademarks](docs/documentation.md#Trademarks)
+  - [Prerequisites](docs/documentation.md#Prerequisites)
+    - [Additional reading](docs/documentation.md#additional-reading)
+  - [Repository structure](docs/documentation.md#repository-structure)
+  - [Building](docs/documentation.md#building)
+  - [Deployment](docs/documentation.md#deployment)
+  - [Running sample applications](docs/documentation.md#running-code-samples-applications)
+  - [Implementing custom ML application](docs/documentation.md#implementing-custom-ML-application)
+  - [Testing and benchmarking](docs/documentation.md#testing-and-benchmarking)
+  - [Troubleshooting](docs/documentation.md#troubleshooting)
+  - [Appendix](docs/documentation.md#appendix)
+  - [Contribution guidelines](docs/documentation.md#contribution-guidelines)
+    - [Coding standards and guidelines](docs/documentation.md#coding-standards-and-guidelines)
+    - [Code Reviews](docs/documentation.md#code-reviews)
+    - [Testing](docs/documentation.md#testing)
+  - [Communications](docs/documentation.md#communications)
+  - [Licenses](docs/documentation.md#licenses)
+