With rapid advancements in the machine learning (ML) space, it’s almost
dizzying the array of new applications suitable for ML. At NXP, we continue to
grow our ML software solutions to support the ever-expanding market.
Let’s look at some of the latest technology advancements we have made
available within our eIQ machine learning software environment.
All Machine Learning applications, whether for cloud, mobile, automotive or
embedded, have these things in common—developers must collect and label
training data, train and optimize a neural network model and deploy that model
on a processing platform. At NXP, our processing platforms don’t
specifically focus on cloud or mobile, but we are seriously enabling machine
learning for embedded applications (industrial and IoT) and automotive
applications (driver replacement, sensor fusion, driver monitoring and ADAS).
eIQ ML Software for IoT and Industrial
In June 2019, we launched our
eIQ ML Software Development Environment
with the primary goal of optimally deploying open source inference engines on
our MCUs and application processors. Today these engines include TensorFlow
Lite, Arm® NN, ONNX runtime and OpenCV and as Figure 1
depicts, these span across all compute engines in one way or another. And
wherever possible, we integrate optimizations into the inference engines (such
as a performance-tuned backend for TensorFlow Lite), targeted at making our
MCUs and applications processors faster. To facilitate customer deployment, we
include these engines along with all necessary libraries and kernels (for example,
CMSIS-NN, Arm Compute Library) in our Yocto BSPs and MCUXpresso software
development kit (SDK).
Figure 1: eIQ™ machine learning software development environment
An important part of our support for these open source inference engines is in
the maintenance of version upgrades; whether they are synchronous (for example, Arm
does quarterly releases of Arm NN and Arm Compute Library) or asynchronous
(Google releases TensorFlow Lite versions whenever warranted). In the
fast-moving world of machine learning, these upgrades and feature enhancements
are important and always deliver better performance, support for more neural
network operators (to allow the use of newer models) and other new features.
The release information, which is much too long to list here, is available on
the GitHub pages for
Figure 2: eIQ Auto™ performance benchmarks
Recently, as machine learning technologies have expanded within NXP, eIQ ML
software has grown to become an umbrella brand representing multiple facets of
machine learning. Further enhancement of eIQ software comes from our
automotive group who recently rolled out the
toolkit, providing an Automotive SPICE® compliant deep
learning toolkit for NXP’s S32V processor family and ADAS development.
This technology aligns with our S32 processors offering functional safety,
supporting ISO 26262 up to ASIL-D, IEC 61508 and DO 178. The inference engine
of the eIQ Auto toolkit includes a backend that automatically selects the
optimum partitioning for the workload of a given neural network model across
all the various compute engines in the device. The eIQ Auto toolkit also
integrates functionality to quantize, prune and compress any given neural
network. Benchmarks indicate that this combined process leads up to 36x
greater performance for given models compared to other embedded deep learning
Over time, we will roll out updated versions and new releases of eIQ ML
software with added features and functionality to bring increased value to
your machine learning applications. Without unveiling too much detail, new eIQ
ML software features will include tools for model optimization (performance
increase and size reduction) and enhancements to make ML software easier to
use. For NXP, this is the future of machine learning—faster, smaller,
easier to use software with increased functionality—all leading to
widespread industry adoption.