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When artificial intelligence (AI) is advancing rapidly, it requires a large amount of computational resources, the addition of carbon, and engineering effort. The demand for machine learning solutions (ML) that allow AI to work on the edge of a network without a hardware load is growing. Most existing AI solutions are not light enough to work on peripheral devices; thus, it is an obstacle.
OmniML eliminates the gap between AI applications and hardware and makes AI more accessible to everyone. It provides compact and scalable machine learning models with excellent performance. It bridges the gap between AI applications and their huge demands on hardware, and accelerates the deployment of AI on the sidelines, especially computer vision. The company’s core product is a model design platform that automates the combined design, learning and deployment of GPUs, AI SoCs and even microcontrollers.
According to the startup, developers will no longer have to manually optimize ML models for chips and individual devices, which will result in faster high-quality AI deployment and hardware awareness that can work anywhere. In its initial partnerships with large commercial customers in multiple vertical markets, OmniML has achieved significant increases in model performance and cost reduction, making ML work on different edge devices ten times faster and saving 50% of the time.
OmniML was founded by Dr. Song Han, a professor at MIT EECS and a serial entrepreneur, Dr. Di Woo, a former Facebook engineer, and Dr. Huizi Mao, the inventor of Stanford’s “deep compression” technology. OmniML develops comprehensive computer vision with AI to improve security and real-time situational awareness with customers in areas such as smart cameras and independent driving. The compression software of the model, which is now being tested in self-propelled vehicles, has the potential to influence a wide range of businesses.
The launch of the ML model has launched its own AI expansion platform for advanced services with $ 10 million seed funding. The funding round was led by GGV Capital. OmniML will use these funds to expand its machine learning team and improve software development.
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