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

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

About This Training

Deep learning (DL), a subset of machine learning (ML), will soon become a crucial technology within vehicles, from vision processing to automated driving. Barriers to full implementation bring complexity and steep costs; however, DL algorithms can assist in these challenges, especially in tasks such as object detection and classification over “traditional” computer vision algorithms.

Learn how to implement and configure the NXP eIQ auto deep learning toolkit, engineered to help customers optimize and implement 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.

Presentation

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

What You Will Learn

Discover how eIQ auto DL toolkit simplifies DL and NN for embedded processing
Learn how to connect to TensorFlow, Pytorch, ONNX and Caffe leading training frameworks
Explore how the eIQ auto DL toolkit partitions networks to improve performance and reduce DL complexity using on-chip computation engines like the Arm^® Cortex^® A, Arm Neon^™ and APEX vision accelerators

Presented By

Phil Pesses

Senior Technical Product Marketing Engineer, NXP Semiconductors
Brent Duff

Senior ADAS Systems and Apps Engineer, NXP Semiconductors

DURATION

59 min

LANGUAGE

English

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Additional Resources

eIQ Auto Deep Learning Toolkit for S32V23x Processors

Related Trainings

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

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