Nota’s deep learning model compression technology allows models to be stored individually on small edge devices, not on a server or cloud, without affecting the performance. So compared to large AI models, our on-device AI technology incurs only 20% of the previous cost and speeds up inference and predictions by 70%. Our compression removes latency, server/network cost, and privacy problems, while still achieving state-of-the-art performance at real-time object detection, identification, and tracking. Clients can benefit from our stand-alone AI products at a lower cost, faster inference, and no privacy concerns. Nota’s on-device AI technology is the solution for a scalable AI service.

Nota was the first to develop deep learning model compression technology domestically this year. Moreover, Nota offers greater compression than any other existing compression technologies over the world based on the same performance level.