Edge machine learning specialist SiMa.ai has developed an embedded edge System-on-Chip machine learning platform. The company’s purpose-built MLSoC is designed to provide low power computer vision applications on embedded and edge platforms. The company is claiming an improvement of 10x power/performance compared with conventional computer vision platforms.

According to SiMa.ai, current computer vision applications employing machine learning require large amounts of power, are difficult to deploy and lack scalability. The company claims it has addressed these shortcomings with its new platform which it says allows “effortless machine learning deployment and scaling at the edge”. SiMa.ai says “any computer vision application is now possible with push-button ML capability, enabling rapid design iterations in minutes, all at 10x better performance per watt”.

“When we started SiMa.ai 3.5 years ago, we set out to deliver a disruptive 10x performance improvement over alternatives and provide a scalable industry-leading ML experience solving computer vision applications,” said Krishna Rangasayee, CEO and Founder, SiMa.ai. “Today we are delighting customers by delivering on that promise and exceeding their expectations. We are excited to take our very first purpose-built software-centric MLSoC to volume production. This first-time-right success was made possible by a great team, fantastic technology partnerships, and our investors. I would like to thank them all for believing in our mission.”

“We’ve seen over a dozen edge processing solutions, and have never seen anything approaching the performance and power efficiency of SiMa.ai’s MLSoC platform,” said Karl Freund, Founder and Principal Analyst at Cambrian-AI Research. “Their solution is an order of magnitude faster and more energy efficient. So far, they are blowing past their customer’s requirements by accelerating the entire vision processing pipeline, not just the ML inferencing portion. Early customers are finding it extremely easy and simple to implement SiMa.ai into their current solutions.”