Nota, which provides technology to optimize AI models, announced that it has closed a $14.7 million Series B funding round. The company’s technology is another important piece of the puzzle when it comes to helping resource-constrained edge devices run applications such as biometric identification.
Participants in the funding round included Stonebridge Ventures, LB Investment, DS Asset, Intervest, and Company K Partners. The fresh funding comes roughly a year after Nota closed its Series A round with $6.7 million. Nota has raised a total of $23 million to date.
“This will be a step-up opportunity for us to reach a more diverse range of industries with our technology which has already gained validation from top-tier enterprises,” states Myungsu Chae, CEO of Nota, in a prepared statement. The technology has been tested in Pyeongtaek, South Korea, where the company deployed a real-time traffic control solution that uses object detection technology to identify traffic volume and automatically adjust traffic signal controls. The company has also partnered with Musma of Korea to develop construction site management systems that uses video systems to track people and machinery to help prevent worksite accidents.
Nota’s products include NetsPresso, a proprietary hardware-aware AutoML platform that automates AI model development process only with datasets. The company claims that NetsPresso, which is has been in the beta testing stage, automates the process of optimizing AI models for different uses and hardware platforms. This translates into a significant reduction in the time required to develop and deploy models, according to Nota.
In addition to the funding news, Nota said that it has released a beta version of NetsPresso Compression Toolkit module which allows deep learning engineers to use a GUI to employ different AI model compression techniques, again allowing them to optimize AI models more quickly than previous manual tuning methods.
Nota’s offerings enable AI model compression that can be tuned by user-selected parameters, including optimization for specific devices based on systems from Nvidia, Raspberry Pi and other providers. Nota claims inference performance can be increased eight-fold while using less power consumption compared to the unoptimized model.
Published originally on BiometricUpdate.com.
AI model | computer vision | edge AI | Nota | startup | venture funding