Fidelity Leads $80 Million Series B in SiMa.ai

SAN JOSE — SiMa.ai, a machine learning company transforming the embedded edge market through high-performance compute at the lowest power, announced an $80 million Series B financing led by Fidelity Management & Research Company with participation from Adage Capital Management. Also joining this round are existing investors Amplify Partners, Dell Technologies Capital, Wing Venture Capital, Alter Venture Partners, and +ND Capital. The round brings the total amount raised to $120 million since the company’s inception in November 2018.

The Series B financing will enable SiMa.ai to productize its first generation machine learning SoC (MLSoC) platform, as well as jumpstart the second generation product architecture and development. In addition, the investment will provide the capital to execute the company’s go-to-market strategy, drive customer success, and expand the team and operations globally. SiMa.ai has been recognized as one of the top ML startups to watch due to its pioneering machine learning platform that provides a highly differentiated ease of use experience through its software-first approach and technology integration that delivers unprecedented performance at the lowest power. The company is partnering with market-leading customers in the areas of robotics, smart cities, autonomous vehicles, medical imaging, and the government sector.

“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,” said Krishna Rangasayee, founder and CEO at SiMa.ai. “We are thrilled to welcome Fidelity and Adage to the SiMa.ai family and are very grateful for their belief in our vision and in our company. The Series B round of funding is further testament to the incredible hard work and dedication of our very talented team and our world-class customers and technology partners who are vested in our joint success. Together, we are scaling machine learning at the embedded edge.”