SAN JOSE, Calif.--(BUSINESS WIRE)--SiMa.ai, the company enabling high performance machine learning to go green, today announced the appointment of Moshe Gavrielov to its Board of Directors. Gavrielov brings decades of experience across public and private sectors to help SiMa.ai disrupt the embedded edge market. In October, the company introduced the SiMa.ai™ MLSoC™, the industry’s first Machine Learning Platform to break the 1000 FPS/W barrier with 10-30x improvement over alternative solutions.
Gavrielov recently served as the President and CEO of Xilinx, where he drove the company through strategic and operational transformations, resulting in financial accomplishments that drove the Xilinx stock up by 4x during this time period, as well as significant market cap expansion approaching a valuation of $20B. Today he also serves on the Board of TSMC and Foretellix, Ltd., and as Chairman of the Board of Wind River Systems, a TPG Company.
“I’m thrilled to welcome Moshe to our Board of Directors,” said Rangasayee. “His deep technology expertise and proven track record driving business growth with start-ups and public companies alike will help guide SiMa.ai to realize our full potential.”
“I truly believe that the future of compute is high performance machine learning at the edge, and today power is the limiter,” said Gavrielov. “Disrupting this market requires a deep understanding of systems and applications, customer intimacy, and a new software-centric architecture. SiMa.ai’s world-class team has all of these attributes and is well positioned to deliver the industry's best high performance machine learning solution at the lowest power. It’s my privilege to join SiMa.ai in its mission to solve the industry’s biggest challenge at the embedded edge.”
SiMa.ai is the company enabling high performance machine learning to go green. Led by a team of industry experts, the company is committed to delivering the highest frames per second per watt solution in the market to its customers – accelerating the proliferation of high performance machine learning inference at very low power in embedded edge applications. Is your ML Green?™
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