Intelligent Memory for Efficient AI Hardware Accelerator

Baker Mohammad

Dr. Baker Mohammad is the director of the System on Chip lab and professor of CIE at Khalifa University. Dr. Baker is a senior member of IEEE and an IEEE CAS Society distinguished lecturer (2023-2024). Before joining Khalifa University, he was a Senior Staff Engineer/Manager at Qualcomm, Austin, TX, USA, for 6-years, where he was engaged in designing high-performance and low-power DSP processors used for communication and multimedia applications. Before joining Qualcomm, he worked for ten years at Intel Corporation on a wide range of microprocessor designs from high-performance server chips > 100Watt (IA-64) to mobile embedded processors low power sub 1 watt (xscale). Baker earned his PhD from the University of Texas at Austin in 2008, his M.S. degree from Arizona State University, Tempe, and his BS degree from the University of New Mexico, Albuquerque, all in ECE. His research interests include VLSI, power-efficient computing, embedded memory and in-memory computing, neuromorphic computing, emerging technology such as Memristor, STTRAM, hardware accelerators for Cyber-Physical Systems and AI.

Baker authored/co-authored over 200 referred journals and conference proceedings, >5 books, >20 US patents, multiple invited seminars/panellists, and the presenter of >3 conference tutorials, including one tutorial on Energy Harvesting and Power Management for WSN at the 2015 (ISCAS). Baker is on the advisory board for the secure systems research center part of the Technology Innovation Institute. Baker is an associate editor for IEEE Transaction on VLSI (TVLSI), IEEE Access, and Scientific Reports journals. Dr Mohammad participates in technical committees at IEEE conferences and reviews for TVLSI, IEEE Circuits and Systems journals. He has received several awards, including the KUSTAR staff excellence award in intellectual property creation, IEEE TVLSI best paper award, 2016 IEEE MWSCAS Myrill B. Reed best paper award, and Qualcomm Qstar award for excellence in performance and leadership. SRC Techon's best session papers for 2016 and 2017 on the community.