Dr. Manar El-Chammas received the B.E. degree in computer and communications engineering from the American University of Beirut, Lebanon, in 2004, and the M.S. and Ph.D. degree in electrical engineering from Stanford University, in 2006 and 2010, respectively, where his research focused on multi-GS/s time-interleaved ADCs.
He joined Texas Instruments in 2010, where he worked on high-speed and high-performance ADCs for wireless infrastructure in both BiCMOS and deep sub-micron CMOS processes, and was the design manager of the High Speed Data Converter group. He then joined Mythic as the Director of Analog Design, where he led the development of the core analog computation engine for neural networks. Afterwards, he worked at an early stage startup architecting novel artificial intelligence inference systems.
He has been granted multiple patents in high-speed ADC design and mixed-signal computation, has published and presented a variety of journal and conference articles (and was the recipient of the best paper award in IEEE BCTM), and has written a book on time-interleaved ADCs. His research interests include highly linear sampling systems, ultra-low power data converter design, efficient computation systems for machine learning, and neural network algorithms.