Xilinx, Inc. (NASDAQ: XLNX) the leader in adaptive and intelligent computing, announced today that it has acquired DeePhi Technology Co., Ltd (DeePhi Tech), a Beijing-based privately held start-up with industry-leading capabilities in machine learning, specializing in deep compression, pruning, and system-level optimization for neural networks.
DeePhi Tech has been developing its machine learning solutions on Xilinx technology platforms, and the two companies have worked closely together since DeePhi Tech’s inception in 2016. DeePhi Tech’s neural network pruning technology has been optimized to run on Xilinx FPGAs, enabling breakthrough performance with best-in-class energy efficiency. Xilinx has been a major investor in DeePhi Tech, alongside other prominent international investors, since 2017.
“We are excited to continue our strong partnership with Xilinx and work even more closely to deliver leading machine learning solutions to our customers in China and around the world,” said Song Yao, CEO of DeePhi Tech.
“Xilinx is accompanying DeePhi Tech along its journey to explore the potential of machine learning and is supporting our innovation as one of our early investors. We look forward to continuing our joint efforts with Xilinx to bring our solutions to the next level in performance,” said Yi Shan, CTO of DeePhi Tech.
“We are thrilled to welcome DeePhi Tech to the Xilinx family and look forward to further building our leading engineering capabilities and enabling the adaptable and intelligent world,” said Salil Raje, executive vice president of Software and IP Products Group at Xilinx. “Talent and innovation are core to realizing our vision. Xilinx will continue to invest in DeePhi Tech to advance our shared goal of deploying accelerated machine learning applications in the cloud as well as at the edge.”
The DeePhi Tech team will continue to operate out of its offices in Beijing, adding to the more than 200 employees Xilinx has in the Greater China Region. The financial terms of the transaction are undisclosed.
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