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With the freshly raised funds, SambaNova plans to accelerate the software capabilities of its next-generation computing platform further
FREMONT, CA: SambaNova Systems, a computing company, focused on building the industry's most advanced AI systems and data-intensive applications from the data center to the edge, raised $250 million in Series C round of financing. The funding round was led by New York-based global investment management corporation BlackRock with participation from existing investors, including Intel Capital, Walden International, GV, Redline Capital, and WRVI Capital.
With the freshly raised funds, SambaNova plans to accelerate the software capabilities of its next-generation computing platform further.
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"Raising $250M in this funding round with support from new and existing investors puts us in a unique category of capitalization," said Rodrigo Liang, co-founder, and CEO, SambaNova Systems. "This enables us to further extend our market leadership in enterprise computing."
SambaNova Systems presents an integrated software and hardware solution with an optimized architecture that enables data flow from algorithms to silicon. The data flow further enables a wide range of compute-intensive applications to run from the data center to the edge. The reconfigurable dataflow architecture of SambaNova Systems enables applications to drive optimized hardware configurations. The constraints of fixed hardware will no longer confine the working of software.
Founded in 2017 by Rodrigo Liang and Stanford Professors Kunle Olukotun and Chris RÃ, SambaNova Systems had announced its Series B round of financing of $150 million in April 2019. Series B funding was led by Intel Capital with participation from GV.
Olukotun, known as the "father of the multi-core processor," is the manager of the Stanford Hydra Chip Multiprocessor (CMP) research project. Ré is an associate professor in the Department of Computer Science at Stanford University and a MacArthur Genius Award recipient. Ré is also associated with the Statistical Machine Learning Group, Pervasive Parallelism Lab, and Stanford AI Lab.
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