FPGA

Hardware Centric Quantized Convolutional Neural Network and Algorithms

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B3 L5 R5209

This thesis addresses the challenges of deploying quantized convolutional neural networks (QCNNs) on resource-constrained edge devices by proposing two novel hardware-software co-design frameworks: one for deriving lightweight, hardware-friendly models validated on FPGA, and another for hardware-aware mixed-precision quantization on compute-in-memory accelerators.

Hesham Omran wins the first phase of the 8th Annual International Microelectronics Olympiad of Armenia

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Hesham Omran, a PhD student in Prof Khaled Salama's research group (Sensors Lab) won the the first phase of the 8th Annual International Microelectronics Olympiad of Armenia.

Yuxiang Liu

PhD candidate in the Networking Research Lab(NETLAB) at King Abdullah University of Science and Technology.

Hesham Omran

Research Staff, Sensors
Hesham Omran worked as a Researcher with the Sensors Lab after obtaining his Ph.D. degree in Electrical Engineering under the supervision of Prof. Khaled Nabil Salama from King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia. He received the B.Sc. (with honors) and M.Sc. degrees from Ain Shams University, Cairo, Egypt, in 2007 and 2010, respectively, both in electrical engineering. From 2008 to 2011, he was a Research and Teaching Assistant with the Integrated Circuits Lab (ICL), Ain Shams University, and a Design Engineer with Si-Ware Systems (SWS)

Abdurauf Abdurakhmanov