NXP backend: Update user guide and docs Readme (pytorch#14852)

This PR updates NXP backend Readmes in backend and examples directories.

-

cc @robert-kalmar @JakeStevens @digantdesai

Author: roman-janik-nxp

backends/nxp/README.md

@@ -5,24 +5,26 @@ This subtree contains the ExecuTorch Backend implementation for the
 
 The eIQ® Neutron NPU is a highly scalable accelerator core architecture providing machine learning (ML) acceleration,
 able to support common and critical tasks for edge AI such as anomaly detection, speech recognition,
-image classification, object detection, facial recognition, image segmentation, and generative AI use cases like 
+image classification, object detection, facial recognition, image segmentation, and generative AI use cases like
 large and small language models (LLMs & SLMs) and text-to-speech (TTS).
-The architecture provides power and performance optimized NPUs integrated with NXP's broad portfolio of 
+The architecture provides power and performance optimized NPUs integrated with NXP's broad portfolio of
 microcontrollers and applications processors.
 
-The eIQ Neutron NPUs offer support for a wide variety of neural network types such as CNN, RNN, TCN and Transformer 
+The eIQ Neutron NPUs offer support for a wide variety of neural network types such as CNN, RNN, TCN and Transformer
 networks, as well as the ability to adapt and scale to new model architectures, topologies and layer types introduced
-to AI workloads. ML application development with the eIQ Neutron NPU is fully supported by the 
+to AI workloads. ML application development with the eIQ Neutron NPU is fully supported by the
 [eIQ machine learning software development environment](https://www.nxp.com/design/design-center/software/eiq-ml-development-environment/eiq-toolkit-for-end-to-end-model-development-and-deployment:EIQ-TOOLKIT).
 The eIQ AI SW Stack provides a streamlined development experience for developers and end-users of NXP products.
+eIQ extensions connect broader AI ecosystems to the edge, such as the NVIDIA TAO extension, which enables developers
+to bring AI models trained and fine-tuned with TAO to NXP-powered edge devices.
 
 
 ## Supported NXP platforms
 At this moment following eIQ® Neutron NPU variants and NXP platforms are supported by the NXP eIQ Neutron Backend:
 
 * **eIQ Neutron N3-64**, available on [i.MX RT700](https://www.nxp.com/products/i.MX-RT700)
 
-In the future the NXP eIQ Neutron Backend will be extended to support [i.MX 9 Application Processors](https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-9-processors:IMX9-PROCESSORS) 
+In the future the NXP eIQ Neutron Backend will be extended to support [i.MX 9 Application Processors](https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-9-processors:IMX9-PROCESSORS)
 with eIQ Neutron NPU, like the [i.MX 95](https://www.nxp.com/products/iMX95).
 
 
@@ -33,7 +35,7 @@ The eIQ Neutron NPU Backend should be considered as prototype quality at this mo
 improvements. NXP and the ExecuTorch community is actively developing this codebase.
 
 ## Neutron Backend implementation and SW architecture
-Neutron Backend uses the eIQ Neutron Converter as ML compiler to compile the delegated subgraph to Neutron microcode. 
+Neutron Backend uses the eIQ Neutron Converter as ML compiler to compile the delegated subgraph to Neutron microcode.
 The Neutron Converter accepts the ML model in LiteRT format, for the **eIQ Neutron N3** class  therefore the Neutron Backend
 uses the LiteRT flatbuffers format as IR between the ExecuTorch and Neutron Converter ML compiler.
 
@@ -44,10 +46,10 @@ uses the LiteRT flatbuffers format as IR between the ExecuTorch and Neutron Conv
       `node_conveters` is structured as single module for each Edge operator.
     * `backend/ir/lib` - automatically generated handlers from LiteRT flatbuffers schema.
     * `backend/ir/tflite_generator` and `backend/ir/tflite_optimizer` handle the serialization
-       of the in-memory built subgraph for delegation into LiteRT/TFLite flatbuffers 
+       of the in-memory built subgraph for delegation into LiteRT/TFLite flatbuffers
        representation. Code taken from the onnx2tflite tool.
-*  `edge_passes` - Various passes operating on Edge dialect level. 
-*  `quantizer` - Neutron Backend quantizer implementation. 
+*  `edge_passes` - Various passes operating on Edge dialect level.
+*  `quantizer` - Neutron Backend quantizer implementation.
 *  `runtime` - Neutron Backend runtime implementation. For running compiled on device.
 *  `tests/` - Unit tests for Neutron backend.
     * `tests/converter/node_converter` - Operator level unit tests.

examples/nxp/README.md

@@ -4,11 +4,11 @@ format and delegate the model computation to eIQ Neutron NPU using the eIQ Neutr
 
 ## Layout
 * `experimental/` - contains CifarNet model example.
-* `models` - demo models instantiation used in examples
+* `models` - various example models.
 * `aot_neutron_compile.py` - script with end-to-end ExecuTorch AoT Neutron Backend workflow.
 * `README.md` - this file.
-* `run_aot_example.sh` - utility script to launch _aot_neutron_compile.py_. Primarily for CI purpose.
-* `setup.sh` - setup script to install NeutronBackend dependencies.
+* `run_aot_example.sh` - utility script for aot_neutron_compile.py.
+* `setup.sh` - setup script for Neutron Converter installation.
 
 ## Setup
 Please finish tutorial [Setting up ExecuTorch](https://pytorch.org/executorch/main/getting-started-setup).
@@ -23,24 +23,24 @@ $ ./examples/nxp/setup.sh
 * MobileNetV2
 
 ## PyTorch Model Delegation to Neutron Backend
-First we will start with an example script converting the model. This example show the CifarNet model preparation. 
-It is the same model which is part of the `example_cifarnet` in 
+First we will start with an example script converting the model. This example show the CifarNet model preparation.
+It is the same model which is part of the `example_cifarnet` in
 [MCUXpresso SDK](https://www.nxp.com/design/design-center/software/development-software/mcuxpresso-software-and-tools-/mcuxpresso-software-development-kit-sdk:MCUXpresso-SDK).
 
-The NXP MCUXpresso software and tools offer comprehensive development solutions designed to help accelerate embedded 
-system development of applications based on MCUs from NXP. The MCUXpresso SDK includes a flexible set of peripheral 
+The NXP MCUXpresso software and tools offer comprehensive development solutions designed to help accelerate embedded
+system development of applications based on MCUs from NXP. The MCUXpresso SDK includes a flexible set of peripheral
 drivers designed to speed up and simplify development of embedded applications.
 
 The steps are expected to be executed from the `executorch` root folder.
 
-1. Run the `aot_neutron_compile.py` example with the `cifar10` model 
+1. Run the `aot_neutron_compile.py` example with the `cifar10` model
     ```commandline
     $ python -m examples.nxp.aot_neutron_compile --quantize \
-        --delegate --neutron_converter_flavor SDK_25_06 -m cifar10 
+        --delegate --neutron_converter_flavor SDK_25_09 -m cifar10
     ```
 
-2. It will generate you `cifar10_nxp_delegate.pte` file which can be used with the MCUXpresso SDK `cifarnet_example` 
+2. It will generate you `cifar10_nxp_delegate.pte` file which can be used with the MCUXpresso SDK `cifarnet_example`
 project, presented [here](https://mcuxpresso.nxp.com/mcuxsdk/latest/html/middleware/eiq/executorch/docs/nxp/topics/example_applications.html#how-to-build-and-run-executorch-cifarnet-example).
 This project will guide you through the process of deploying your PTE model to the device.
 To get the MCUXpresso SDK follow this [guide](https://mcuxpresso.nxp.com/mcuxsdk/latest/html/middleware/eiq/executorch/docs/nxp/topics/getting_mcuxpresso.html),
-use the MCUXpresso SDK v25.06.00. 
+use the MCUXpresso SDK v25.09.00.