VIZZIO partners ASRock on digital twins
- October 7, 2024
- William Payne
Large-scale digital twin provider VIZZIO Technologies is partnering industrial AI edge computing specialist ASRock Industrial, a subsidiary of ASROCK and PEGATRON. The collaboration aims to develop digital twin and LIVE 3D security surveillance applications for smart cities and AIoT industrial manufacturing.
VIZZIO will employ its AI-based geospatial digital twin technology, EARTH ENGINE, to create accurate and detailed digital replicas of large environments. The company’s subsidiary, POLYTRON, is introducing AI-powered spatial-aware 3D 360 cameras designed for creating digital twins. ASRock Industrial’s Robust Edge AIoT Platform will provide computing power to handle complex 3D rendering, 2D and 3D computer vision, real-time data processing & analytics.
The collaboration will include edge-based video analytics for smart manufacturing, industrial 4.0 applications, traffic management, urban planning, and emergency response systems. The OpenVINO edge-based analytics platform enables industrial AI-driven defect detection, predictive maintenance, and robotic vision, optimising production lines and reducing downtime.
Jon Lee, CEO of POLYTRON.AI & VIZZIO.AI, said “By combining our advanced 3D modelling capabilities with ASRock Industrial’s powerful Intel-based hardware with OPEN VINO, we are delivering powerful inferencing capabilities and real-time edge-based video analytics, enabling businesses to process data closer to the source. This approach ensures faster decision-making, reduces latency, and enhances performance for critical applications in industries ranging from smart manufacturing to security and surveillance. Combining REAL-TIME 3D Scene and POLYTRON LIVE 3D, we optimise and deploy deep learning models across various Intel hardware, such as CPUs, integrated GPUs, FPGAs, and VPUs. The OPEN VINO toolkit is designed to help developers accelerate AI inference workloads, particularly in computer vision, natural language processing, and other machine learning tasks at the edge and in cloud environments