Practical Challenges and Proposed Solutions for Implementing Embedded AI on Arm Cortex A/M Devices

  • Ganesh Balamitran, Renesas Electronics

With the rapid growth in AI and machine vision, the industry’s focus is quickly turning to deploying neural networks for inference on end-point devices, like Arm Cortex A microprocessors (MPUs) and even Cortex M MPUs. In this session, we will share our learnings on the challenges of using 1) various implementations of popular networks on full frameworks like TensorFlow and Caffe, as well as 2) customized/optimized code better suited for embedded MPUs. We will also explore some of the new tool approaches and new tools that are addressing these challenges.

  • Date:Tuesday, October 16
  • Time:11:30 AM - 12:20 PM
  • Location:Executive Ballroom 210E
  • Session Type:Conference Session
  • Room:Executive Ballroom 210E
  • Pass Type:All-Access Pass
  • Secondary Track:Embedded Software Development
Ganesh Balamitran
Renesas Electronics