fbpx
Now available—the Embedded Vision Summit On-Demand Edition! Gain valuable computer vision and edge AI insights and know-how from the experts at the 2021 Summit.

Nota

www.nota.ai

Nota Inc. provides on-device AI solutions that remove the need for any server or cloud computing. With our deep learning model compression techniques, including quantization, pruning, knowledge distillation, and filter decomposition, vision-based models can be stored individually on small edge devices without performance degradation. Our compression removes latency, server/network cost, and privacy problems, while still achieving state-of-the-art performance at object detection, identification, tracking, etc.

Nota recently succeeded in developing the Automatic Model Compression (AMC) platform that automatically compresses the size of computer vision models based on the optimization of the model, target hardware, and the dataset. AMC will eventually free human labor while achieving a higher compression ratio, greater accuracy, and faster inference. We believe our compression platform will accelerate the deployment of on-device computer vision solutions to every aspect of society.

Recent Content by Company

Free Webinar Explores Deep Learning Model Optimization Techniques for Enhanced On-device AI

On March 23, 2021 at 9 am PT (noon ET), Bibek Chaudhary, research engineer at Nota, will present the free half-hour webinar “Selecting and Combining Deep Learning Model Optimization Techniques for Enhanced On-device AI,” organized by the Edge AI and Vision Alliance. Here’s the description, from the event registration page: Model optimization techniques such as …

Free Webinar Explores Deep Learning Model Optimization Techniques for Enhanced On-device AI Read More +

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

Contact

Address

1646 North California Blvd.,
Suite 360
Walnut Creek, CA 94596 USA

Phone
Phone: +1 (925) 954-1411
Scroll to Top