Technology

Xnorization enables a new world of devices

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Beyond the Traditional

Traditional deep learning AI models are compute intensive. They rely on GPU’s, and are often restricted to running in data-centers in the cloud. Our novel approach of Xnorization re-trains state-of-the-art machine learning models to run efficiently in resource-constrained environments without compromising accuracy.

We build with hardware in mind

We build state-of-the-art models designed to run smartly and efficiently in resource constrained environments. Our team of deep learning researchers work side-by-side with our core engineers and hardware engineers to constantly iterate and optimize, ensuring models reach stunning levels of efficiency across a range of hardware platforms.

We empower devices across the hardware spectrum

A visual graphic of how Xnor empowers hardware across a spectrum of devices

Whether you're building for IoT devices or providing cloud AI services, our ultra efficient AI models allow you to get more from your hardware

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AI so efficient it can run on sunlight

AI models running on a $5 FPGA the size of quarter.

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Face Recognition

Face Recognition

See our lightning-fast real-time Face Recognition models in action!

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Object Detection

YOLO: YOU ONLY LOOK ONCE

The inventors of YOLO

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Multi-Model

Binarization and Xnor-Net

AI models processed with binary operations

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See our Technology in Action

On-device Face Recognition

Real-Time Person Detection

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