With the business scenarios escalating, there is more of a demand to upgrade technology.
The chip runs high-speed neural network calculations
Ruff Face ID has machine vision and audio abilities, when it comes to practical business scenarios, it is able to operate data storage and use edge computing. It also has a convolution artificial neural network hardware accelerator which manages the calculations more efficiently.
In the case of high real-time requirements, the demand from cloud to edge is magnified. Critical applications for face recognition and tracking, local data processing and edge computing will become a necessity.
Ruff Face ID’s AI chip enables off-line processing for edge calculations and advanced machine learning models for deep neural networks, including video frames, speech synthesis, time series data, and cameras, microphones, and other data generated by sensors or equipment.
Based on edge computing, the integration of the Internet of Things and AI, it is more efficient and provides rapid responses in business scenarios, while reducing the storage and running costs of cloud data, especially some repetitive and low-value data.
For corporate clients and manufacturers in the process of utilizing intellectual technology, there will be a variety of complex obstacles between data and devices, including technology to modules, modules to equipment, to data then to the cloud. It takes significant time and cost to overcome these obstacles.
Ruff Face ID provides a face-tracking module and cloud services, the cloud service supplies users with SaaS, which allows them to use remote device management including updating firmware, updating algorithms, push notifications
Ruff Face ID is able to handle real-time data utilizing edge and cloud services to reduce cost, as there is not need to invest in additional hardware.