Edge intelligence is one of the most disruptive innovations since the advent
of the Internet of Things (IoT). While the IoT gave rise to billions of smart,
connected devices transmitting countless terabytes of sensor data for AI-based
cloud computing, another revolution was underway: machine learning (ML) on
edge devices. As more and more intelligence migrates to the network edge, NXP
has embraced this trend by delivering cost-, performance- and power-optimized
processing solutions to propel ML technology across multiple markets and
applications, providing end users with the benefits of enhanced security,
greater privacy and lower latency.
Developing ML, deep learning and neural network applications traditionally has
been the domain of data scientists and AI experts. But this is changing as
more ML tools and technologies have become available to abstract away some of
the complexity of developing machine learning applications. A case in point is
NXP’s eIQ (“edge intelligence”) ML development environment. eIQ provides a
comprehensive set of workflow tools, inference engines, neural network (NN)
compilers, optimized libraries and technologies that ease and accelerate ML
development for users of all skill levels, from embedded developers embarking
on their first deep learning projects to experts focused on advanced object
detection, classification, anomaly detection or voice recognition solutions.
Introduced in 2018, eIQ ML software has evolved to support system-level
application and ML algorithm enablement targeting NXP’s i.MX Series, from
low-power i.MX RT crossover microcontrollers (MCUs) to multicore i.MX 8 and
i.MX 8M applications processors based on Arm®
Cortex®-M and Cortex®-A cores.
Today’s Big Update
To help ML developers become even more productive and proficient on NXP’s i.MX
8 processing platforms, we’ve significantly expanded our eIQ software
environment to include new eIQ Toolkit workflow tools, GUI-based eIQ Portal
development environment and the DeepViewRT™ inference engine
optimized for i.MX and i.MX RT devices.
Figure 1. High-level presentation of the eIQ toolkit and eIQ portal features
Let’s take a closer look at how these powerful, new additions to the eIQ
software environment can help streamline ML development, enhance productivity,
and give developers more options and greater flexibility.
eIQ ToolKit: Enabling “ML for Everyone”
Given the underlying complexities of machine learning, neural network and deep
learning applications—and the varying needs of ML developers—a simple
“one-size-fits-all” tool isn’t the answer. A better approach is to provide a
comprehensive and flexible toolkit that scales to meet the skill and
experience levels of ML developers. To this end, we’ve added the powerful yet
easy-to-use eIQ Toolkit to the eIQ ML development environment, enabling
developers to import datasets and models and train, prune, quantize, validate
and deploy neural network models and ML workloads across NXP’s i.MX 8M family
of applications processors and i.MX RT Crossover MCU portfolio. Whether you
are an embedded developer starting out on your first ML project, a proficient
data scientist or an AI expert, you’ll find the right toolkit capabilities to
match your skill level and streamline your ML project.
Figure 2. eIQ portal provides a dataset curator to help you annotate and
organize all your training data.
The eIQ Toolkit provides straightforward workflows and ML application
examples. The Toolkit also provides an intuitive, GUI-based development option
with the eIQ Portal and the option of using command-line host tools if
you prefer. If you want to leverage off-the-shelf development solutions or
need professional services and support from NXP or one of our trusted
partners, the toolkit provides easy access to a growing list of options in our
eIQ Marketplace from companies like
Figure 3. eIQ portal provides a convenient approach to model validation and
Using the eIQ Portal, you can easily create, optimize, debug, convert and
export ML models, as well as import datasets and models from TensorFlow, ONNX
and PyTorch ML frameworks. You can train a model with your data with the
“bring your own data” (BYOD) flow, select from a database of pre-trained
models or import a pre-trained model with the “bring your own model” (BYOM)
flow such as the advanced detection models from
Au-Zone Technologies. By following the simple BYOM process, you can build a trained model using
public or private cloud-based tools and then transfer the model into the eIQ
Toolkit to run on the appropriate silicon-optimized inference engine.
Figure 4. eIQ portal provides a flexible approach for BYOM and BYOD.
On-target, graph-level profiling capabilities provide developers with runtime
insights to fine-tune and optimize system parameters, runtime performance,
memory usage and neural network architectures for execution on i.MX devices.
Revving up NXP’s Latest eIQ Inference Engine
At the heart of a machine learning development project is the inference engine
– the runtime component of ML applications. In addition to supporting
inference with a variety of open-source, community-based inference engines
optimized for i.MX devices and MCUs such as Glow, ONNX and TensorFlow Lite, we
have added the DeepViewRT inference engine to our eIQ ML software development
Developed in collaboration with our partner
Au-Zone Technologies, DeepViewRT is a proprietary inference engine providing a stable, longer
term vendor-maintained solution that complements open community-based
Figure 5. The DeepViewRT provides a stable, production-ready, and flexible inference engine for ML applications.
The DeepViewRT inference engine is available as middleware in NXP’s MCUXpresso
SDK and Yocto BSP release for Linux™
More to Love About eIQ
The eIQ ML development environment with all essential baseline enablement
including the new eIQ Toolkit, eIQ Portal and eIQ inference with DeepViewRT
are provided with no license fees.
Hone Your Skills at the NXP ML/AI Training Academy
ML/AI Training Academy
provides self-paced learning modules on various topics related to ML
development and best practices in using eIQ tools with NXP’s i.MX and i.MX RT
devices. The ML/AI Training Academy is open to all NXP customers and features
an expanding collection of training modules to help you get started with your
ML application development. Learn more at
Get Started With eIQ ML Software and Tools Today
The eIQ Toolkit, including the eIQ Portal, is available for download now with
a single click at
eIQ ML software including the DeepView RT inference engine for i.MX
applications processors is supported on the current Yocto Linux release. eIQ
ML software for i.MX RT crossover MCUs is fully-integrated into NXP’s
MCUXpresso SDK release.
Learn more at
Join the eIQ Community:
eIQ Machine Learning Software.
And learn more about DeepViewRT and Au-Zone’s ML development tools