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.
The Simplifying Deep Learning (DL) and Neural Networks (NN) for Embedded Processing webinar will show how to implement and configure the NXP eIQ
Auto deep learning 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.
REGISTER TODAY in one of our two sessions and do not miss any data-driven opportunity!
· (Tuesday) March 3 @ 9:00 am PST
· (Thursday) April 2 @ 10:00 am CEST
During this 45-minute webinar, Phil Pesses (Senior Technical Product Marketing Engineer – Automotive Processing, NXP semiconductors) will show how the eIQ Auto deep learning toolkit simplifies DL and NN for Embedded Processing by connecting to TensorFlow, Pytorch, ONNX, and Caffe leading training frameworks. He will demonstrate how the eIQ Auto 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.