Hi and welcome to another in the Arm “What Is” program series. In each episode,
we dive into a tech topic to give you insight and perspective into some of the hottest design
trends today. I’m Brian Fuller, editor-in-chief at Arm and today we’re going to explore Tiny ML,
which, if you’re unfamiliar, is not something an entomologist worries about!
Machine learning is the subset of artificial intelligence in which devices themselves learn
from data to perform tasks better.
Today, to help us understand Tiny ML are two giants in the field, Dennis Laudick and Steve
Roddy. Dennis is head of AI and machine learning marketing at Arm, and Steve is head of products
for AI and ML. Here’s something you might not know about these gentlemen: Dennis is an
avid rower and runner and has lived for many years in China. Steve is currently hunkered
down in a mountain hideaway where he enjoys working with chainsaws and burning debris.
Let's dive right in. I’m going to start with Dennis: Give us a better description of machine
learning if you don't mind and then tell us about TinyML in that context? And why should we care?
Yeah so, machine learning is often interchanged with artificial intelligence nowadays. So
artificial intelligence is and machine's ability to learn things, there's many ways to do that,
but what is causing all the excitement at the moment is the methodology of machine learning.
And machine learning is about creating a model using lots and lots of data, a model
to represent something so the classic example is a cat. You use thousands of pictures of different
things to let a machine figure out what a cat is. Then you use that model to test against a new
picture and it can tell you whether or not it's a cat or not with pretty high level of certainty so
quite exciting stuff, very complex. And we were struggling with this in computer science and
now we can do it. And it applies to a huge range of applications so, which is great.
Now tiny ML is something I'm particularly excited about. And this is a lot of the original machine
learning was happening in the cloud and the experimentation is happening there,
but over the course of the last five years, it started to move more and more into devices
and we started working on use cases and applications
in the small space and the device spaces around you. And this is starting to go everywhere,
but to do things in the modern technology world you kind of need a community. You need a community
of knowledge and capabilities of input and that's kind of what tiny ML is. Tiny ML is a community
of people interested in bringing this amazing technology down into the devices around us.
Got it. Steve, they say good things come in small packages. How tiny is "tiny"?
Good question. Pretty much any place you have a microcontroller today – and there
are tens of billions of microcontrollers shipped annually. – you can run machine learning workloads
in the device, at the source of the information. As Dennis was saying when machine learning took
off in the last few years, all the excitement and all the experimentation was in the data center
because it was all the inventing new algorithms, new models that
could recognize things with greater and greater accuracy exceeding human ability.
And then people realized well you know in order to actually run the model, you need to be close to
where the data is. So think of like you are on an elevator where you got sensors on the motors and
the shafts and the chains and the pulleys that you want to know when the device is getting
dangerously close to failing; you want to be able to do a preventative maintenance; you want to
listen for vibrations and motors and things. Well, the place to do it is there where the data is,
where the device happens to be. And if you have a microcontroller controlling something
and you're sampling data, you know, very low hertz rate, you can run a
small machine learning model and listen for variability in the motor and realize "oh,
it's about to fail" so I need to shut my production line down or turn my conveyor
belt off. I need to stop the escalator in the shopping mall before something bad happens. So
pushing the capability to the very source of where the data comes from and being able to recognize
sounds or voices or pictures or patterns. The tiny part refers to the small
endpoint processing, but as part of an overall system. So think managing traffic in a city with
lots and lots of intersections and lots and lots of traffic lights. You are going to have some
global level of optimization, but you are going to have some localized optimization as well.
Dennis, how do you see this technology evolving in the next few years and what kind of expanded
use cases might we see? Yeah, this is quite an interesting one. So Steve and I we've been working
on this area for a number of years now and to be honest with you, it's still accelerating. The
learning here, what we can do with this technology is continuous to go to more and more places.
I have heard it referred to as software 2.0, I have heard it referred to as a universal function
generator, which you know in computer science a lot of what we are trying to do is to create
complex algorithms and functions. So having this tool is incredible. We see it going everywhere,
literally every device category Arm is involved in. We see machine learning happening in
more unique and unexpected ways. So I'm really amazed now that machine
learning is really starting to happen down at the edge and the endpoint,
I'm quite excited to see where this is going to go. It's going to really change things.
Well thanks guys! I think we think know a lot more about TinyML than we did just a few minutes ago.
Check out all our other “What Is” episodes here and be sure to subscribe to this channel
because we’ll be adding more episodes as the year progresses. Thanks for listening!