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Easing the Deployment of Deep Learning Algorithms to an Automotive Embedded Processor with NXP eIQ^® Auto

Easing the Deployment of Deep Learning Algorithms to an Automotive Embedded Processor with NXP eIQ^® Auto

About This Training

Automotive engineers are facing several hurdles to port their deep learning (DL) algorithms to an embedded hardware target while delivering the optimum performance for their applications and adhering to the production boundary conditions applicable to their system. DL applications are quickly growing across the globe in use throughout the vehicle. In fact, the DL segment for artificial intelligence (AI) in the automotive market is expected to grow by over 45% CAGR from 2020 to 2026.

Explore ways of dealing with these challenges and view an example of an optimized workflow for deploying deep learning in automotive production vehicles using the NXP eIQ auto deep learning toolkit.

Presentation

Easing the Deployment of Deep Learning Algorithms to an Automotive Embedded Processor with NXP eIQ Auto

What You Will Learn

Understand the engineering challenges while deploying Deep Learning algorithms
Address engineering shortages when developing for embedded processors
Identify the performance gains when using an optimized inference framework
Explore the evaluation and production of an automotive AI framework
Learn how to utilize the NXP eIQ Auto toolkit through a demo application example

Presented by

Mark O'Donnell

ADAS Global Marketing Manager, NXP Semiconductors
Jonathan Hills

Senior ADAS Software Engineer, NXP Semiconductors

DURATION

38 min

LANGUAGE

English

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Additional Resources

eIQ Auto Deep Learning Toolkit for S32V23x Processors

Related Trainings

Simplifying Deep Learning and Neural Networks for Embedded Processing with eIQ Auto

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