FREMONT, Calif., Aug. 4, 2017 /PRNewswire/ -- NVXL Technology is pleased to announce that Dr. Sharad Malik has joined the NVXL Technical Advisory Board as the company prepares to introduce revolutionary technology for disaggregated storage and compute acceleration.
Dr. Malik is a pioneer in design methodology and automation for computing systems. His research in functional timing analysis and propositional satisfiability has been widely used in industrial electronic design automation tools. Dr. Malik received his Ph.D. in Computer Science from the University of California, Berkeley. He has been awarded the DAC Award as an author of the most cited DAC paper in first 50 years of the conference. He is also the recipient of the ICCAD Most Influential Paper Award and the CAV Award for fundamental contributions to the development of high-performance Boolean satisfiability solvers. Dr. Malik's current work focuses on developing a methodology for the design of accelerator-based platforms.
Dr. Malik is currently the Chair of the Department of Electrical Engineering at Princeton University. As part of the Technical Advisory Board, he will advise the NVXL engineering team in their development of leading-edge compute acceleration systems. "I'm very excited to be working with NVXL" said Dr. Malik, "I think the NVXL disaggregated approach to compute and storage acceleration is very promising."
"We are thrilled to be working with Dr. Malik" said Claudionor Coelho, VP of Software Engineering – Machine Learning & Orchestration at NVXL Technology, "As a leading expert in developing design methodologies for future generations of computing systems, his guidance will be invaluable to our team."
NVXL will be announcing more additions to the Advisory Board in the coming week, ahead of the company's presence at the Flash Memory Summit in Santa Clara, CA on August 8-10th. NVXL will be exhibiting their compute accelerated server solutions in Booth #806.
NVXL is developing a revolutionary compute acceleration platform enabling tomorrow's applications, deep learning, and machine learning.
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SOURCE NVXL Technology