OmniML, a San Jose, CA-based startup developing smaller and faster machine learning models, raised $10m in seed funding.
The round was led by GGV Capital with participation from Qualcomm Ventures, Foothill Ventures, and a few other venture capital firms.
The company intends to use the funds to accelerate the use of artificial intelligence (AI) on edge devices.
Founded by Dr. Song Han, MIT EECS professor and serial entrepreneur, Dr. Di Wu, former Facebook engineer, and Dr. Huizi Mao, co-inventor of the “deep compression” technology coming out of Stanford University, OmniML enables and empowers smaller, scalable machine learning (ML) models on edge devices to be more capable of performing AI inference at levels that are impossible today outside of data centers and cloud environments and make AI more accessible for everyone, not just data scientists and developers.
The company is working with customers in sectors such as smart cameras and autonomous driving to create AI-enabled advanced computer vision for improved security and real-time situational awareness. This technology, though, is broadly applicable—for instance, it can improve the retail customer experience and support safety and quality control detection for precision manufacturing.