This manual page is dedicated to introduction NXP eIQ Neutron backend. NXP offers accelerated machine learning models inference on edge devices. To learn more about NXP's machine learning acceleration platform, please refer to the official NXP website.
ExecuTorch v1.0 supports running machine learning models on selected NXP chips (for now only i.MXRT700). Among currently supported machine learning models are:
- Convolution-based neutral networks
- Full support for MobileNetV2 and CifarNet
- Hardware with NXP's i.MXRT700 chip or a evaluation board like MIMXRT700-EVK.
- MCUXpresso IDE or MCUXpresso Visual Studio Code extension
- MCUXpresso SDK 25.06
- eIQ Neutron Converter for MCUXPresso SDK 25.06, what you can download from eIQ PyPI:
$ pip install --index-url https://eiq.nxp.com/repository neutron_converter_SDK_25_06
Instead of manually installing requirements, except MCUXpresso IDE and SDK, you can use the setup script:
$ ./examples/nxp/setup.sh
To test converting a neural network model for inference on NXP eIQ Neutron backend, you can use our example script:
# cd to the root of executorch repository
./examples/nxp/aot_neutron_compile.sh [model (cifar10 or mobilenetv2)]For a quick overview how to convert a custom PyTorch model, take a look at our example python script.
To learn how to run the converted model on the NXP hardware, use one of our example projects on using ExecuTorch runtime from MCUXpresso IDE example projects list. For more finegrained tutorial, visit this manual page.
→{doc}nxp-partitioner — Partitioner options.
→{doc}nxp-quantization — Supported quantization schemes.
→{doc}tutorials/nxp-tutorials — Tutorials.
→{doc}nxp-dim-order — Dim order support (channels last inputs).
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:caption: NXP Backend
nxp-partitioner
nxp-quantization
tutorials/nxp-tutorials
nxp-dim-order