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This document will cover some of the most commonly asked questions we've gotten about eIQ and embedded machine learning.

Anything requiring more in-depth discussion/explanation will be put in a separate thread. All new questions should go into their own thread as well

What is eIQ?

The NXP® eIQ™ machine learning (ML) software development environment enables the use of ML algorithms on NXP EdgeVerse™ microcontrollers and microprocessors, including MCX-N microcontrollers, i.MX RT crossover MCUs, and i.MX family application processors. eIQ ML software includes a ML workflow tool called eIQ Toolkit, along with inference engines, neural network compilers and optimized libraries. This software leverages open-source and proprietary technologies and is fully integrated into our MCUXpresso SDK and Yocto development environments, allowing you to develop complete system-level applications with ease.

eIQ also enables models to use the new eIQ Neutron NPU found on the MCX-N microcontroller devices and upcoming future NPU enabled embedded devices. 


How much does eIQ cost?

Free! NXP is making eIQ freely available as a basic enablement to jumpstart ML application development. It is also royalty free. 


What devices are supported by eIQ?

eIQ is available for the following i.MX application processors:

eIQ is available for the following MCX MCUs:

eIQ is available for the following i.MX RT crossover MCUs:


What inference engines are available in eIQ?

i.MX apps processors and i.MX RT MCUs support different inference engines. The best inference engine can depend on the particular model being used, so eIQ offers several inference engine options to find the best fit for your particular application. 


Inference engines for i.MX:

  • TensorFlow Lite (Supported on both CPU and GPU/NPU)
  • ARM NN (Supported on both CPU and GPU/NPU)
  • OpenCV (Supported on only CPU)
  • ONNX Runtime (Currently only supported on CPU)

Inference engines for MCX and i.MX RT




Can eIQ run on other MCU devices?

There's no special hardware module required to run eIQ inference engines and it is possible to port the inference engines to other NXP devices. 


What is eIQ Toolkit?

eIQ Toolkit enables machine learning development with an intuitive GUI (named eIQ Portal) and development workflow tools, along with command line host tool options as part of the eIQ ML software development environment. 

The eIQ Portal is an intuitive graphical user interface (GUI) that simplifies ML development. Developers can create, optimize, debug and export ML models, as well as import datasets and models, rapidly train and deploy neural network models and ML workloads.

eIQ Toolkit also includes the Neutron Converter Tool that is used to convert quantized TensorFlow Lite models so they can make use of the eIQ Neutron NPU found on newer NXP devices like the MCX N family. 

Is eIQ Toolkit required to use eIQ inference engines? 

No, eIQ Toolkit is optional enablement from NXP to make it easier to generate vision-based models that can then be used with the eIQ inference engines. However if you already have your model development flow in place, or want to use pre-created models from a model zoo, you can use those models with eIQ inference engines as well. 


What is the eIQ Neutron NPU? 

The new eIQ Neutron NPU is a Neural Processing Unit developed by NXP which has been integrated into the upcoming MCX N and i.MX95 devices, with many more to come. It was designed to accelerate neural network computations and significantly reduce model inference time. The scalability of this module allows NXP to integrate this NPU into a wide range of devices all while having the same eIQ software enablement. 

For more details on the NPU for MCX N see this Community post.

How can I start using the eIQ Neutron NPU? 

There are hands-on NPU lab guides available that walk through the steps for converting and running a model with the eIQ Neutron NPU. 

How can I get eIQ?

For MCU devices:

eIQ inference engines are included as part of MCUXpresso SDK for supported devices. Make sure to select the “eIQ” middleware option.

There is an additional optional software packages:

  • eIQ Toolkit - for model creation and conversion. Includes the GUI model creation tool eIQ Portal and eIQ Neutron Converter Tool for eIQ Neutron NPU enabled devices like MCX N. 

For i.MX devices:

eIQ is distributed as part of the Yocto Linux BSP. Starting with the 4.19 release line there is a dedicated Yocto image that includes all the Machine Learning features: ‘imx-image-full’. For pre-build binaries refer to i.MX Linux Releases and Pre-releases pages.

There are is also an optional software package: 

  • eIQ Toolkit - for model creation and conversion. Includes the GUI model creation tool eIQ Portal


What documentation is available for eIQ?

For i.MX RT and MCX N devices: 

There are user guides inside the \middleware\eiq\doc folder after downloading the MCUXpresso SDK from the MCUXpresso SDK builder. Documentation for eIQ Toolkit can be found inside the eIQ Toolkit documentation folder after installation, typically at C:\NXP\eIQ_Toolkit_v1.10.0\docs


For i.MX devices:

The eIQ documentation for i.MX is integrated in the Yocto BSP documentation. Refer to i.MX Linux Releases and Pre-releases pages.

  • i.MX Reference Manual: presents an overview of the NXP eIQ Machine Learning technology.
  • i.MX Linux_User's Guide: presents detailed instructions on how to run and develop applications using the ML frameworks available in eIQ (currently ArmNN, TFLite, OpenCV and ONNX).
  • i.MX Yocto Project User's Guide: presents build instructions to include eIQ ML support (check sections referring to ‘imx-image-full’ that includes all eIQ features).

It is recommended to also check the i.MX Linux Release Notes which includes eIQ details.


For i.MX devices, what type of Machine Learning applications can I create? 

Following the BYOM principle described above, you can create a wide variety of applications for running on I.MX. To help kickstart your efforts, refer to PyeIQ – a collection of demos and applications that demonstrate the Machine Learning capabilities available on i.MX.

  • They are very easy to use (install with a single command, retrieve input data automatically)
  • The implementation is very easy to understand (using the python API for TFLite, ArmNN and OpenCV)
  • They demonstrate several types of ML applications (e.g., object detection, classification, facial expression detection) running on the different compute units available on i.MX to execute the inference (Cortex-A, GPU, NPU).


Can I use the python API provided by PyeIQ to develop my own application on i.MX devices?

For developing a custom application in python, it is recommended to directly use the python API for ArmNN, TFLite, and OpenCV. Refer to the i.MX Linux User’s Guide for more details.


You can use the PyeIQ scripts as a starting point and include code snippets in a custom application (please make sure to add the right copyright terms) but shouldn’t rely on PyeIQ to entirely develop a product.


The PyeIQ python API is meant to help demo developers with the creation of new examples.


What eIQ example applications are available for i.MX RT?

eIQ example applications can be found in the <SDK DIR>\boards\<board_name>\eiq_examples directory: 




What are Glow and DeepViewRT inference engines in the MCUXpresso SDK? 

These are inference engines that were supported in previous versions of eIQ but are now deprecated as new development has focused on TensorFlow Lite for Microcontrollers. These projects are still available in MCUXpresso SDK 2.15 for legacy users, but it is highly recommended that any new projects use TensorFlow Lite for Microcontrollers.  


How can I learn more about using TensorFlow Lite with eIQ?

There is a hands-on TensorFlow Lite for Microcontrollers lab available.

There is also a i.MX TensorFlow Lite Lab that provide a step-by-step guide on how to get started with eIQ for TensorFlow Lite for i.MX devices. 


How can I learn more about using eIQ Toolkit to generate a model?

There are hands-on eIQ Toolkit labs available that provide a step-by-step guide to get started with generating a vision based model with eIQ Toolkit. 


What application notes are available to learn more about eIQ?

What is the advantage of using eIQ instead of using the open-sourced software directly from Github?

eIQ supported inference engines work out of the box and are already tested and optimized, allowing for performance enhancements compared to the original code. eIQ also includes the software to capture the camera or voice data from external peripherals. eIQ allows you to get up and running within minutes instead of weeks. As a comparison, rolling your own is like grinding your own flour to make a pizza from scratch, instead of just ordering a great pizza from your favorite pizza place. 



Does eIQ include ML models? Do I use it to train a model?

eIQ is a collection of software that allows you to Bring Your Own Model (BYOM) and run it on NXP embedded devices. eIQ provides the ability to run your own specialized model on NXP’s embedded devices. 

For those new to AI/ML, we also now offer eIQ Toolkit which can be used to generate new vision based AI models using images provided to the tool. 

There are several inference engine options like TensorFlow Lite and Glow that can be used to run your model. MCUXpresso SDK and the i.MX Linux releases come with several examples that use pre-created models that can be used to get a sense of what is possible on our platforms, and it is very easy to substitute in your own model into those examples.


I’m new to AI/ML and don’t know how to create a model, what can I do?

A wide variety of resources are available for creating models, from labs and tutorials, to automated model generation tools like eIQ ToolkitGoogle Cloud AutoMLMicrosoft Azure Machine Learning, or Amazon ML Services, to 3rd party partners like SensiML and Au-Zone that can help you define, enhance, and create a model for your specific application.

Alternatively if you have no interest in generating models yourself, NXP also offers several pre-built voice and facial recognition solutions that include the appropriate models already created for you. There are Alexa Voice Services, Local voice control, and face and emotion recognition solutions available. Note that these solutions are different from eIQ as they include the model as well as the appropriate hardware and so those devices are sold as unique part numbers and have a cost-optimized BOM to directly use in your final product.

  • eIQ is for those who want to use their own model or generate a model themselves using eIQ Toolkit.
  • The three solutions mentioned above are for those who want a full solution (including model) already created for them for those specific applications.

I’m interested in anomaly detect or time series models on microcontrollers, where can I get started?

The ML-based System State Monitor Application Software Pack provides an example of gathering time-series data, in this case vibrations picked up by an accelerometer, and includes Python scripts to use the data that was collected to generate a small model that can be deployed on many different microcontrollers (including i.MX RT1170, LPC55S69, K66F) for anomaly detection. The same concepts and technique can be used for any sort of times series data like magnetometers, pressure, temperature, flow speed, and much more. This can simplify the work of coming up with a customer algorithm to detect the different states of whatever system you're interested in, as you can let the power of machine learning figure all that out for you. 




Why do I get an error when running Tensorflow Lite Micro that it "Didn't find op for builtin opcode"?

The full error will look something like this:

Didn't find op for builtin opcode 'PAD' version '1'

Failed to get registration from op code ADD

Failed starting model allocation.

AllocateTensors() failed
Failed initializing model

The reason is that with MCUXpresso SDK, the TFLM examples have been optimized to only support the operands necessary for the default models. If you are using your own model, it may use extra types of operands. The way to fix this is described in the TFLM Lab Guide on how to use the All Ops Resolver. Add the following header file


and then comment out the micro_op_resolver and use this instead: 

//tflite::MicroOpResolver &micro_op_resolver = //MODEL_GetOpsResolver(s_errorReporter);

tflite::AllOpsResolver micro_op_resolver;

Why do I get the error "Incompatible Neutron NPU microcode and driver versions!" when using the Neutron NPU?

The version of the eIQ Neutron Converter Tool needs to be compatible with the NPU libraries used by your project. See more details in this post on using custom models with eIQ Neutron NPU. 

How do I use my GPU when training with eIQ Toolkit?

eIQ Toolkit 1.10 only supports GPU training on Linux due to the latest TensorFlow versions no longer supporting GPU on Windows. 

Why do I get a blank or black LCD screen when I use the eIQ demos that have camera+LCD support on RT1170 or RT1160?

There are different versions of the LCD, so you need to make sure you have the software configured correctly for the LCD you have. See this post for more details on what to change.

Why is the inference speed slow when using TensorFlow Lite for Microcontrollers?

Make sure you are using the "Release" project configuration which enables high compiler optimizations. This significantly reduces the inference time for TFLM projects. Glow and DeepViewRT projects are not affected by this setting because they use pre-compiled binaries and pre-compiled libraries respectively. 


General AI/ML:

What is Artificial Intelligence, Machine Learning, and Deep Learning?

Artificial intelligence is the idea of using machines to do “smart” things like a human. Machine Learning is one way to implement artificial intelligence, and is the idea that if you give a computer a lot of data, it can learn how to do smart things on its own. Deep Learning is a particular way of implementing machine learning by using something called a neural network. It’s one of the more promising subareas of artificial intelligence today.


This video series on Neural Network basics provides an excellent introduction into what a neural network is and the basics of how one works. 


What are some uses for machine learning on embedded systems?

Image classification – identify what a camera is looking at

  • Coffee pods
  • Empty vs full trucks
  • Factory defects on manufacturing line
  • Produce on supermarket scale

Facial recognition – identifying faces for personalization without uploading that private information to the cloud

  • Home Personalization
  • Appliances
  • Toys
  • Auto

Audio Analysis

  • Wake-word detection
  • Voice commands
  • Alarm Analytics (Breaking glass/crying baby)

Anomaly Detection

  • Identify factory issues before they become catastrophic
  • Motor analysis
  • Personalized health analysis


What is training and inference?

Machine learning consists of two phases: Training and Inference


Training is the process of creating and teaching the model. This occurs on a PC or in the cloud and requires a lot of data to do the training. eIQ is not used during the training process.


Inference is using a completed and trained model to do predictions on new data. eIQ is focused on enhancing the inferencing of models on embedded devices.


What are the benefits for “on the edge” inference?

When inference occurs on the embedded device instead of the cloud, it’s called “on the edge”. The biggest advantage of on the edge inferencing is that the data being analyzed never goes anywhere except the local embedded system, providing increased security and privacy. It also saves BOM costs because there’s no need for WiFi or BLE to get data up to the cloud, and there’s no charge for the cloud compute costs to do the inferencing.  It also allows for faster inferencing since there’s no latency waiting for data to be uploaded and then the answer received from the cloud.


What processor do I need to do inferencing of models?

Inferencing simply means doing millions of multiple and accumulate math calculations – the dominant operation when processing any neural network -, which any MCU or MPU is capable of. There’s no special hardware or module required to do inferencing. However specialized ML hardware accelerators, high core clock speeds, and fast memory can drastically reduce inference time.


Determining if a particular model can run on a specific device is based on:

  • How long will it take the inference to run. The same model will take much longer to run on less powerful devices. The maximum acceptable inference time is dependent on your particular application.
  • Is there enough non-volatile memory to store the weights, the model itself, and the inference engine
  • Is there enough RAM to keep track of the intermediate calculations and output


As an example, the performance required for image recognition will be very dependent on the model is being used to do image recognition. This will vary depending on how many classes, what size of images to be analyzed, if multiple objects or just one will be identified, and how that particular model is structured. In general image classification can be done on i.MX RT devices and multiple object detection requires i.MX devices, as those models are significantly more complex.


eIQ provides several examples of image recognition for i.MX RT and i.MX devices and your own custom models can be easily evaluated using those example projects. 


How is accuracy affected when running on slower/simpler MCUs?

The same model running on different processors will give the exact same result if given the same input. It will just take longer to run the inference on a slower processor.


In order to get an acceptable inference time on a simpler MCU, it may be necessary to simplify the model, which will affect accuracy. How much the accuracy is affected is extremely model dependent and also very dependent on what techniques are used to simplify the model.


What are some ways models can be simplified?

  • Quantization – Transforming the model from its original 32-bit floating point weights to 8-bit fixed point weights. Requires ¼ the space for weights and fixed point math is faster than floating point math. Often does not have much impact on accuracy but that is model dependent.
  • Fewer output classifications can allow for a simpler yet still accurate model
  • Decreasing the input data size (e.g. 128x128 image input instead of 256x256) can reduce complexity with the trade-off of accuracy due to the reduced resolution. How much that trade-off is depends on the model and requires experimentation to find.
  • Software could rotate image to specific position using classic image manipulation techniques, which means the neural network for identification can be much smaller while maintaining good accuracy compared to case that neural network has to analyze an image that could be in all possible orientations.


What is the difference between image classification, object detection, and instance segmentation?

Image classification identifies an entire image and gives a single answer for what it thinks it is seeing. Object detection is detecting one or more objects in an image. Instance segmentation is finding the exact outline of the objects in an image.


Larger and more complex models are needed to do object detection or instance segmentation compared to image classification.



What is the difference between Facial Detection and Facial Recognition?

Facial detection finds any human face. Facial recognition identifies a particular human face. A model that does facial recognition will be more complex than a model that only does facial detection. 



How come I don’t see 100% accuracy on the data I trained my model on?

Models need to generalize the training data in order to avoid overfitting. This means a model will not always give 100% confidence , even on the data a model was trained on.


What are some resources to learn more about machine learning concepts? 

Labels (2)

Hi @anthony_huereca ,

I am using eIQ tool on model training for i.MXRT1170 platform.

I find eIQ tool is always use PC CPU resource, so when it perform the model training that will occupy around 60 ~ 80% CPU resource. But it not use any GPU resource.

Is there any method to configure eIQ tool to use GPU resource on the model training ? In case that will have better performance on the AI training. Is it?





Hi David,

  eIQ Toolkit 1.0.5 is using TensorFlow version is 2.3.2, so to use the GPU when training you will need to install cuDNN v7.6 and CUDA 10.2. If you have newer versions of those tools installed on your PC you may need to uninstall those first before installing the version needed for the 1.0.5 version of eIQ Toolkit. I've also updated the FAQ with this information and it will be in the documentation in the next version of eIQ Toolkit that is released.


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‎03-25-2024 11:50 AM
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