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.