The demand for artificial intelligence-powered apps is growing, and so are the benefits of dedicated chips. The market for edge chips is booming because of the hundreds of millions of dollars in venture capital financing. The last of the dozens of upstarts vying for customers grabbed $136 million in October as it doubled down on new opportunities.
Sima.ai is a company that is developing a system-on-chip platform for artificial intelligence applications. Sima.ai began demoing an accelerator that combines traditional compute and machine learning from Arm with a custom machine learning accelerator and dedicated vision accelerator, linked via a proprietary interconnect.
Sima.ai closed a $30 million additional investment from Fidelity Management and Research Company with participation from previous investors and Lip-Bu Tan, who will join the board.
The funding will be used to accelerate scaling of the engineering and business teams globally, and to continue investing in both hardware and software innovation. He has a long history of investing in deep tech companies that have gone on to disrupt industries.
He was the general manager of the company's overall business. An engineer by trade, Rangasaye was the COO at Groq and once headed product planning at Altera, which Intel acquired in 2015.
I founded Sima.ai with two questions: What are the biggest challenges in scaling machine learning to the embedded edge and how can we help?
Sima.ai wants to work with companies that are developing a lot of things. The company opened a design center in India and collaborated with the University of TFC;bingen to identify artificial intelligence hardware and software solutions for ultra-low cost.
The second-generation architecture is being worked on by over 100 employees of Sima.ai.
Sima.ai's software and hardware platform can be used to scale machine learning to embedded edge applications. Sima.ai's software and hardware platform has the flexibility to be used to address these, even though these applications will use many diverse computer vision pipelines with a variety of machine learning models.
Sima.ai's challenges remain mass manufacturing its chips affordably and beating back the many rivals in the edge artificial intelligence computing space. The startup is planning to ship mass-produced production volumes of its first chip by the year's end. According to Markets and Markets, edge computing will be a $6.72 billion market in the next four years. Some analysts predict that the deep learning market will reach $66.3 billion by the year 2025.
Over the past decade, machine learning has had a profound impact on the cloud and mobile markets, and the next battleground is the multi-trillion-dollar embedded edge market. Sima.ai's unique architecture, market understanding and world-class team has put them in a leadership position.
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