Measuring embedded machine learning
Who is this presentation for?
- Developers and data scientists
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.
- Experience with Python and C (useful but not required)
What you'll learn
- Get started with TensorFlow Lite on embedded hardware
- Learn to use machine learning inferencing to perform common tasks
- Recognize speech and other audio signals on embedded hardware
Babilim Light Industries
Alasdair Allan is a director at Babilim Light Industries and a scientist, author, hacker, maker, and journalist. An expert on the internet of things and sensor systems, he’s famous for hacking hotel radios, deploying mesh networked sensors through the Moscone Center during Google I/O, and for being behind one of the first big mobile privacy scandals when, back in 2011, he revealed that Apple’s iPhone was tracking user location constantly. He’s written eight books and writes regularly for Hackster.io, Hackaday, and other outlets. A former astronomer, he also built a peer-to-peer autonomous telescope network that detected what was, at the time, the most distant object ever discovered.
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