This California-based AI startup is developing smaller, faster machine learning models to bridge the gap between AI applications and a diverse range of devices found at the edge

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As artificial intelligence (AI) advances rapidly, it requires a large amount of computing resources, carbon footprint, and engineering effort. There is a growing demand for machine learning (ML) solutions that allow AI to run at the edge of the network without overwhelming the hardware. Most existing AI solutions are not lightweight enough to run on edge devices; it is therefore an obstacle.

OmniML bridges the gap between AI applications and cutting-edge hardware, making AI more accessible to everyone. It enables compact and scalable machine learning models with excellent performance. It bridges the gap between AI applications and the huge demands they place on hardware and accelerates the deployment of edge AI, especially computer vision. The company’s core product is a pattern design platform that automates the co-authoring, training, and deployments of patterns 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, resulting in faster deployment of high-performance, hardware-aware AI that can run anywhere. or. In its first collaborations with large enterprise customers in many vertical markets, OmniML has achieved significant increases in model performance and reduced costs, with ML tasks running ten times faster on various edge devices and gaining 50% time.

OmniML was founded by Dr. Song Han, a professor at MIT EECS 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 advanced AI-enabled computer vision for enhanced safety and real-time situational awareness with customers in areas 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 $10 million in seed funding. 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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