Unlock Machine Learning on Edge Devices with eIQ™ Software Development Environment

Unlock Machine Learning on Edge Devices with eIQ™ Software Development Environment

As director of AI/ML technologies for NXP, I’m very excited to announce general availability of NXP’s eIQ machine learning (ML) software development environment. eIQ ML software is available now with inference engines and libraries leveraged from the tremendous advancements in open source machine learning technologies. We have deployed and optimized these technologies, such as CMSIS-NN, TensorFlow, TensorFlow Lite, OpenCV, and Arm NN, for our popular i.MX RT and i.MX applications processors, and are easily accessed through NXP’s development environments for MCUXpresso and Yocto (Linux) to provide seamless support for your application development. Furthermore, eIQ software is accompanied by sample applications in object detection and voice recognition, to provide you with a starting point in their deployment of machine learning at the edge.

Machine Learning is on the rise, but it’s just the tip of the iceberg

As ML and Artificial Intelligence (AI) migrates towards the edge, one of the biggest challenges is deployment on resource-constrained devices, especially if you’ve been building your ML applications in the cloud. To run your models directly on edge devices, those models must be optimized and matched to an inference engine supporting the specific compute engines (i.e. CPU, GPU, DSP, ML accelerator). eIQ ML software solves this challenge, making it easy for you to integrate complex hardware components and providing the expertise for machine learning vision and voice applications.

Build your trained models using public or private cloud-based tools, then simply bring your models into the eIQ software environment to generate the appropriate inference engine. The eIQ software integration into our MCUXpresso and Yocto environments, takes care of all ML software dependencies, including the necessary hardware abstraction layers that connect the advanced machine learning technology to the underlying compute engines – bridging all the tools needed to bring your ML models to production.

eIQ software allows for ML applications and use cases to have a life of its own, as it opens the door to mass market implementations of ML-enabled edge devices.

Getting Started: BYOM – Bring Your Own Machine Learning Model  

You can easily get started with eIQ ML software by creating and bringing your model. Then install MCUXpresso SDK or access NXP’s Yocto build environment, gather what’s needed for ML inferencing within those packages, build your application, then load it onto the hardware to run it.

For more information on NXP eIQ ML software development environment visit: https://www.nxp.com/support/developer-resources/software-center/eiq-ml-development-environment:EIQ

Markus Levy
Markus Levy
Markus Levy joined NXP in 2017 as the Director of AI and Machine Learning Technologies. In this position, he is focused primarily on the technical strategy, roadmap, and marketing of AI and machine learning capabilities for NXP's microcontroller and i.MX applications processor product lines. Previously, Markus was chairman of the board of EEMBC, which he founded and ran as the President since April 1997. Mr. Levy was also president of the Multicore Association, which he co-founded in 2005. Previously, he was Senior Analyst at Microprocessor Report and an editor at EDN magazine. Markus began his career at Intel Corporation, as both a Senior Applications Engineer and customer training specialist for Intel's microprocessor and flash memory products. Markus volunteered for thirteen years as a first responder - fighting fires and saving lives.

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