Binary convolutional neural networks
Xnorized models deliver state-of-the-art performance
optimizes AI performance
from battery powered
devices to GPUs
Deep learning at the edge
How do we drive deep learning models all the way down to 1 bit?
Traditional deep learning models for convolutional network uses 32-bit precision floating point operations processed on GPUs, which are generally cost prohibitive for everyday products.
Xnor reduces the precision down to a single bit and processes it using binary operations like XNOR and pop-count, which are standard instructions on low cost CPU platforms.
To maintain high accuracy while binarizing the parameters, we train the models with proprietary learning and optimization techniques to deliver state-of-the-art performance while utilizing the least amouint of CPU and memory resources. This allows high performance machine learning models to run well in any environment, from the most resource-constrained, low cost battery-operated MCUs to high-end GPUs and servers.
You Only Look Once
Xnor's founding team developed YOLO, a leading open source object detection model used in real world applications. We use a proprietary, high performance, binarized version of YOLO in our models for enterprise customers.
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 hardware platforms ranging from simple ARM-based microcontrollers to the most powerful x86 server CPUs to application-specific FPGAs and neural net accelerators.
AI enable your product with Xnor
Xnor delivers custom AI solutions tailored for your needs
- We can create and train new binary models using your existing training datasets
- We can start with your ML models and convert them into highly efficient binary neural network models
- If desired, we can annotate your raw images and videos to create training datasets
- Our custom models run on our lightweight and highly optimized proprietary inference engine
How can we build AI for you?