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As Artificial Intelligence (AI) advances quickly, it demands a vast amount of computational resources, carbon footprint, and engineering efforts. There is a growing demand for machine learning (ML) solutions that enable AI to run at the network’s edge without overburdening the hardware. Most existing AI solutions aren’t light enough to run on edge devices; thus, this is a hurdle.
OmniML bridges the gap between AI applications and edge hardware, making AI more accessible to everyone. It empowers compact, scalable machine learning models with excellent performance. It bridges the gap between AI applications and the enormous demand they impose on hardware and speeds the deployment of AI on edge – particularly computer vision. The company’s main product is a model design platform that automates model co-creation, training, and deployments for GPUs, AI SoCs, and even microcontrollers.
According to the startup, developers will no longer have to manually optimize ML models for individual chips and devices, which will result in the speedier deployment of high-performance, hardware-aware AI that can run anywhere. In its initial collaborations with large business customers in numerous vertical markets, OmniML achieved significant increases in model performance and cost reduction, with ML jobs running ten times quicker on various edge devices and 50 percent time savings.
OmniML was founded by Dr. Song Han, an MIT EECS professor and serial entrepreneur, Dr. Di Wu, a former Facebook engineer, and Dr. Huizi Mao, co-inventor of Stanford’s “deep compression” technology. OmniML is developing AI-enabled advanced computer vision for improved security and real-time situational awareness with customers in industries such as smart cameras and autonomous driving. Its model compression software, which is currently being tested in self-driving cars, has the potential to impact a wide range of businesses.
The ML-model deployment startup has launched its AI deployment platform for edge services with a seed funding of $ 10 Million. The funding round was led by GGV Capital. OmniML will use these funds to expand its machine learning team and improve its software development.
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