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@@ -52,30 +52,53 @@ by displaying different performance metrics such as inference cycle count estima ## 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), +The evaluation kit primarily supports [Arm® Corstone™-300](https://developer.arm.com/ip-products/subsystem/corstone/corstone-300) +and [Arm® Corstone™-310](https://developer.arm.com/ip-products/subsystem/corstone/corstone-310) reference packages as its +primary targets. 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. +Abstraction Layer (HAL). The application common code can be run on native host machine (x86_64 or aarch64) 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-U NPU driver](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-core-driver) 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. +NPU, to the CPU. If the operator is supported, [CMSIS-NN](https://github.com/ARM-software/CMSIS_5) is used to optimise +CPU workload execution with int8 data type. Else, TensorFlow™ Lite for Microcontrollers' reference kernels are used as +a final fall-back. 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. +A high level overview of the different components in the software, and the platforms supported out-of-the-box, is shown +in the diagram below. +  +For a more detailed description of the build graph with all major components, see [Building](./docs/documentation.md#building). + +### Reusable software + +There are source files in the repository that form the core of the Machine Leaning flow for all the use cases. These +are exposed as APIs that the examples can use and even be combined to form chained use cases. The API sources are +designed to be portable across platforms and provide functionality for preprocessing of data, running an inference, and +postprocessing of results. These allow a common flow for all use cases with minor differences in how each of these +blocks are instantiated. + +As an independent CMake project, these APIs can be used by or integrated into other projects easily. We also produce +[CMSIS Packs](https://developer.arm.com/tools-and-software/embedded/cmsis/cmsis-packs) with these sources, so they +could be used in all tools/IDEs (for example, +[Arm® Development Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Development%20Studio) and +[Keil® µVision®](https://www2.keil.com/mdk5/uvision/)) that support the use of CMSIS Packs. + +### Getting started + To run an ML application on the Cortex-M and Ethos-U NPU, please, follow these steps: -1. Setup your environment by installing [the required prerequisites](./docs/sections/building.md#Build-prerequisites). +1. Set up 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. |