A Balanced Approach to Inference Implementation at the Edge

  • Markus Levy, NXP Semiconductors

The IoT led to an explosion of data. Though it was once thought the cloud would handle this data, the industry soon realized this was impractical. As a result, machine learning on the edge has increased in popularity and usefulness. One of the most fascinating things about machine learning, or edge computing, is that it can be accomplished on a wide range of device categories, from MCUs with Arm Cortex-M4 and M7 cores to complex SoCs with high-end A-class cores, GPUs, and ML accelerators.

This presentation describes the cost and performance tradeoffs for these device categories, using Vision, Voice, and Vibration as examples.

  • Date:Thursday, October 18
  • Time:9:00 AM - 9:50 AM
  • Location:Executive Ballroom 210E
  • Session Type:Conference Session
  • Room:Executive Ballroom 210E
  • Pass Type:All-Access Pass
Markus Levy
NXP Semiconductors