Measuring embedded machine learning

Description

While machine learning is traditionally associated with heavy-duty power-hungry processors, the future of machine learning is on the edge and on small, embedded devices that can run for a year or more on a single coin-cell battery. Deep learning can be very energy efficient, and allows us to makes sense of sensor data. It can turn raw accelerometer data into information about whether a machine is working well or malfunctioning or even recognize voice commands from a microphone feed. The ability to run trained networks “at the edge,” nearer the data without the cloud — or even without even a network connection —means that you can interpret sensor data in real time, pulling signal from the data without storing potentially privacy-infringing data in the cloud.

Alasdair Allan explores how to use machine learning on your own problems, whether you’re using your laptop, a Raspberry Pi, or an ARM Cortex M microcontroller. He looks at the new hardware intended to speed up machine learning inferencing on the edge and gives you some benchmarks as to which platform is fastest.