NXP eIQ Neutron Backend#
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.
Features#
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
Target Requirements#
Hardware with NXP’s i.MXRT700 chip or a evaluation board like MIMXRT700-EVK.
Development Requirements#
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
Using NXP eIQ Backend#
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.
Runtime Integration#
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.
Reference#
→Partitioner API — Partitioner options.
→NXP eIQ Neutron Quantization — Supported quantization schemes.
→NXP Tutorials — Tutorials.
