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Xnor’s saltine-sized, solar-powered AI hardware redefines the edge

“If AI is so easy, why isn’t there any in this room?” asks Ali Farhadi, founder and CEO of Xnor, gesturing around the conference room overlooking Lake Union in Seattle. And it’s true — despite a handful of displays, phones and other gadgets, the only things really capable of doing any kind of AI-type work are the phones each of us have set on the table. Yet we are always hearing about how AI is so accessible now, so flexible, so ubiquitous.

And in many cases, even those devices that can aren’t employing machine learning techniques themselves, but rather sending data off to the cloud where it can be done more efficiently. Because the processes that make up “AI” are often resource-intensive, sucking up CPU time and battery power.

That’s the problem Xnor aimed to solve, or at least mitigate, when it spun off from the Allen Institute for Artificial Intelligence in 2017. Its breakthrough was to make the execution of deep learning models on edge devices so efficient that a $5 Raspberry Pi Zero could perform state of the art computer vision processes nearly as well as a supercomputer.

The team achieved that, and Xnor’s hyper-efficient ML models are now integrated into a variety of devices and businesses. As a follow-up, the team set their sights higher — or lower, depending on your perspective.

Answering his own question on the dearth of AI-enabled devices, Farhadi pointed to the battery pack in the demo gadget they made to show off the Pi Zero platform and explained: “This thing right here. Power.”

Power was the bottleneck they overcame to get AI onto CPU- and power-limited devices like phones and the Pi Zero. So the team came up with a crazy goal: Why not make an AI platform that doesn’t need a battery at all? Less than a year later, they’d done it.

That thing right there performs a serious computer vision task in real time: It can detect in a fraction of a second whether and where a person, or car, or bird, or whatever, is in its field of view, and relay that information wirelessly. And it does this using the kind of power usually associated with solar-powered calculators.

The device Farhadi and hardware engineering head Saman Naderiparizi showed me is very simple — and necessarily so. A tiny camera with a 320×240 resolution, an FPGA loaded with the object recognition model, a bit of memory to handle the image and camera software and a small solar cell. A very simple wireless setup lets it send and receive data at a very modest rate.

“This thing has no power. It’s a two-dollar computer with an uber-crappy camera, and it can run state of the art object recognition,” enthused Farhadi, clearly more than pleased with what the Xnor team has created.

For reference, this video from the company’s debut shows the kind of work it’s doing inside: