Optimizing IoT End Nodes for Machine-Learning

  • Shawn Prestridge, IAR Systems AB

This session will explore the challenges of coding and debugging complex machine-learning (ML) and artificial intelligence (AI) systems. We will cover how to boost code performance and how to use the advanced debugging and trace capabilities in conjunction with the Neural Network Kernels for Arm Cortex-M software library (CMSIS-NN). We will also look at why code quality is such a major issue with machine learning and how you can future-proof your source code. This session is a must-attend for embedded developers that want to improve their development processes for ML and AI in automotive, industrial, medical, and other applications.

  • Date:Wednesday, October 17
  • Time:9:00 AM - 9:50 AM
  • Location:Executive Ballroom 210E
  • Session Type:Conference Session
  • Room:Executive Ballroom 210E
  • Pass Type:All-Access Pass
  • Secondary Track:Embedded Software Development