The Dummies Guide to Edge AI
Tags: Technology, Raspberry Pi, Edge Computing, Artificial Intelligence, DIY

With all this talk of edge, AI, and inference, what does it all mean? Fret not, dear AIKEA user, here’s a short guide to all the tech in AIKEA

A guide for the astronaut dog in all of us

Edge — noun

a line or border at which a surface terminates: a brink or verge:

Artificial Intelligence — noun

the capacity of a computer to perform operations analogous to learning and decision making in humans. Abbreviation: AI, A.I.

But what does it mean when it’s slapped together? When it comes to edge AI or edge computing, you can say it is the opposite to the idea of the cloud. Smart devices such as your Echo, your Ring, or Nest, are smart because they have the full power of Amazon and Google’s servers at their disposal to do the complex calculations the AI needs on the cloud. In exchange for its free use however, they collect data on your habits at home to better target you for ads or sell you more stuff.

The team of AIKEA however, has managed to squeeze (Jus de Raspberry Pi, anyone?) all the power of a beloved mini computer to do all the AI on site, or on the edge. Giving you the smart functions you want without needing to have your data sent and calculated elsewhere.

So how does the AI in AIKEA work?

Fair warning, this is gonna get a bit technical and quite nerdy.

The reason why AIKEA can do what it does, is that we upload a simplified AI model onto the Raspberry Pi so it can infer things. But more on that later.

Back in 2017, Google released to the public what it had been cooking up for the past 2 years, a little something called TensorFlow.

Now you’ve probably heard snippets about it before, but long story short, what it does is allow developers to build neural networks: large databases to represent interconnected data. And for the first time, it made these artificial learning tools accessible to engineers, developers, and scientists.

In other words, you would be able to “train” a database to learn patterns and build relationships between your data and your desired results.

The only downside was that it required massive amounts of computing power, something that would be easier for Google, Amazon, Facebook, and Microsoft to get, but for the rest of us, not so much. It was in hopes of truly bringing free, open-source, AI to everyone, that BerryNet was created.

BerryNet initially started out as a response to a challenge: “What is the smallest computer that we can run AI on?” in 2016. However as BerryNet matured and the slew of privacy violations by Big Data became exposed, suddenly, the importance of edge AI and data protection became more than a thought experiment, but a critical component in adapting to our new social paradigm.

Under the Hood

On the software side, BerryNet uses adapted object classification and detection as the core of the visual edge AI applications. Taking these models, the BerryNet Team has re-trained them via transfer learning, and then optimized their models to run faster and more efficiently. And just as importantly, enabled to be used on the many different engines such asTensorFlow Lite, OpenVino, TensorRT, and many other popular engines.

Classification vs detection

Why pick the Raspberry Pi

Thankfully, chip manufacturers like ARM, having long predicted the need of more compact and powerful hardware to do AI have been hard at work building tools for developers like BerryNet. And with the popularity of the Raspberry Pi in projects from plant watering timers to robots, we thought: what better way to introduce AI to this community, than through a computer they already have at home?

And what’s even better? The Raspberry Pi foundation just recently came out with their Pi 4! Allowing object inference speeds to reach breakneck speeds, and at an unmatched price point.