Xilinx announces a new Data Center Ecosystem Investment Program to be administered through its corporate venture arm, Xilinx Technology Ventures. Investments made through this initiative will focus primarily on technologies that expand Xilinx’s data center products and offerings and ignite industry innovation, time to market advantage, and lower overall cost of ownership.
The new program targets solutions for emerging workload applications such as machine learning, image and video processing, data analytics, storage data base acceleration, and network acceleration.
“Through this investment program, we want to help enable start-ups that create libraries, middleware, and application software to accelerate the broad deployment of Xilinx FPGA solutions in the Data Center,” said Greer Person, senior director, Corporate Business Development. “In addition to funding, our portfolio companies often gain access to Xilinx business and technology experts, products, and design environments to help them create more competitive solutions, accelerate time-to-market, and reduce development costs.”
“Our customers are broadly deploying Xilinx FPGAs for application acceleration. We recognize the need for vibrant ecosystems that will spark innovation and support the full breadth and depth of solutions our markets require,” said Hemant Dhulla, vice president of Data Center and Wired Communications Business at Xilinx. “I am pleased that we are increasing our investment activity with promising young companies that will drive ingenuity and contribute to ecosystem growth and TeraDeep is the perfect example of a company doing just that.”
As part of this program, Xilinx recently completed its first data center ecosystem investment in TeraDeep, a company specializing in convolutional neural networks-based machine learning. TeraDeep is widely recognized for its state-of-the-art deep learning expertise and acceleration technology which runs on Xilinx FPGAs. Through this investment, TeraDeep will continue to work closely with Xilinx to further optimize its solutions on Xilinx-based FPGA boards.