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 eIQTM auto deep learning toolkit.
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
- The evaluation and production of an automotive AI framework
- Utilizing the NXP eIQ Auto toolkit through a demo application example