Sima.ai, a company developing embedded edge hardware for machine learning applications, today announced that it raised $80 million in a series B round led by Fidelity Management & Research Company. The startup says that the funds will be used to commercialize its first-generation system-on-a-chip product, as well as to jumpstart development of its second-generation product’s architecture and supports Sima.ai’s go-to-market, customer success, and hiring initiatives globally.
Edge computing is forecast to be a $6.72 billion market by 2022, according to Markets and Markets. Its growth will coincide with that of the deep learning chipset market, which some analysts predict will reach $66.3 billion by 2025. There’s a reason for these rosy projections — edge computing is expected to make up roughly three-quarters of the total global AI chipset business in the next six years.
After emerging from stealth in late 2019, Sima.ai unveiled what it calls its “machine learning system-on-chip” platform: an AI accelerator chipset designed with low power requirements and support for fast inferencing. The company says that its hardware’s performance ranges between 50 TOPS (theoretical operations per second) to 200 TOPS at 5 Watts to 20 Watts, delivering what Sima.ai claims is an “industry first” of 10 TOPS per Watt.
“[Our chip] combines traditional compute IP from Arm with our own machine learning accelerator and dedicated vision accelerator … By combining multiple machine learning accelerator mosaics via a proprietary interconnect, we can scale from 50 TOPs at 5 Watts up to [a theoretical] 400 TOPs at 40 Watts,” Kavitha Prasad, VP of business development and system applications at Sima.ai, explained in a blog post last year. “While it’s capable of a wide range of ML workloads such as natural language processing, SiMa.ai’s [chip] is initially optimized for computer vision applications.”
Sima.ai aims to work with customers in robotics, smart cities, autonomous vehicles, medical imaging, and government. The company claims to have completed several early customer engagements and recently announced the opening of a design center in Bengaluru, India, which Sima.ai says will support engineering and operations while launching job opportunities for board development, operations, infrastructure, and system application roles.
“The embedded edge is a multi-trillion dollar market and still using decades old technology. Sima.ai is poised to disrupt this massive market with our differentiated machine learning technology and approach,” founder and CEO Krishna Rangasayee said in a press release.
It’s worth noting that Sima.ai has plenty in the way of competition. Startups AIStorm, Hailo, Esperanto Technologies, Quadric, Graphcore, Xnor, and Flex Logix are developing chips customized for AI workloads — and they’re far from the only ones. Mobileye, the Tel Aviv company Intel acquired for $15.3 billion in March 2017, offers a computer vision processing solution for AVs in its EyeQ product line. And Baidu last July unveiled Kunlun, a chip for edge computing on devices and in the cloud via datacenters.
But Sima.ai appears to be well-capitalized with $120 million in funding to date, having closed a $30 million financing round in May 2020 led by Dell Technologies Capital. The company plans to tape out its chipset early this year with the goal of delivering engineering samples and potentially customer samples toward the end of 2021.
Beyond Fidelity, Adage Capital Management, Amplify Partners, Dell, Wing Venture Capital, Alter Venture Partners, and +ND Capital participated in San Jose, California-based Sima.ai’s series B.
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