Simplifying Deep Learning (DL) and Neural Networks (NN) for Embedded Processing

Date: Thursday, April 2, 2020
Time: 10:00 AM Central European Time

Deep Learning (DL), a subset of Machine Learning, is quickly becoming a crucial technology within vehicles: from vision processing to automated driving —the DL market is expected to reach USD 18.16 Billion by 2023. DL offers better accuracy and maintainability in tasks such as object detection and classification over “traditional” computer vision algorithms, but the barriers to full implementation bring complexity and steep costs.

This webinar will show how to implement and configure the NXP eIQ™ Auto DL toolkit for optimizing and implementing DL without the need for customized hardware expertise. The eIQ Auto toolkit quantizes, prunes, and compresses Neural Networks (NN) by partitioning workload and selecting the optimum hardware to compute engines on the MPU.


Philip Pesses, Senior Technical Product Marketing Engineer – Automotive Processing – NXP Semiconductors

Phil Pesses is a product marketing professional with almost 20 years of experience with a strong understanding of ADAS systems. He is responsible for the product marketing and go-to-market plans for processors and microcontrollers targeted for the automotive and distribution markets.

Phil has a Bachelor’s Degree in Electrical Engineering from the University of Texas at Austin.