Shawahna, Ahmad I. A.

Ph.D. in Computer Engineering

"The more you know, the more you realize you know nothing" - Socrates

Ahmad Shawahna was born in Jenin, Palestine. He received the Ph.D. and master’s degrees in computer engineering from King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, in 2022 and 2016, respectively. He also obtained the bachelor’s degree in computer engineering from An-Najah National University, Palestine, in 2012.
He is currently a research scientist with the Center for Communications and Information Technology Research, Research Institute, KFUPM. He also worked for the computer engineering department at KFUPM as a teaching assistant. Additionally, he worked for Art Technologies in Palestine as a software engineer (mobile applications developer). Finally, he worked in web application development at Dot Learning Center, Palestine, in 2011.
His research interests include digital design with Verilog, machine learning, hardware accelerators, FPGAs, computer vision, and quantization. He has published several articles in high-quality journals and international conferences.
About image

Without careful implementation of today’s complex deep neural network models,

The design may not fit the target edge device due to limited computational and logical resources.

My research goal is to design algorithms and specialized hardware modules for addressing the requirements for efficient implementations of convolutional neural networks (CNNs) on field-programmable gate array (FPGA) platforms. I aim to build a novel end-to-end automated framework that domain experts can rely on for optimizing CNN implementation on FPGAs in terms of throughput, latency, energy efficiency, and power consumption. Toward this end, my research provides a mechanism for partitioning available FPGA resources to design multiple high-throughput hardware accelerators for convolutional layer operations. Furthermore, for CNN models to be applicable for deployment on resource-constrained battery-powered edge devices, I am interested in quantizing CNN tensors and building computationally efficient models while maintaining the application’s level requirements.
To date, my work has provided a comprehensive review of recent existing techniques and architectures for implementing deep neural networks (DNNs) on FPGAs and provided recommendations for future directions that will simplify the use of FPGA-based accelerators and enhance their performance. I have also developed an optimized convolutional layer processor (CLP) and an accompanying metaheuristic-based algorithm for designing an FPGA-based multi-CLP accelerator, showing a significant improvement in performance (1.31x - 2.37x higher throughput than the state-of-the-art single-/multi-CLP approaches). I further showed that different CNN layers have different properties related to the quantization process. Therefore, I have worked on heterogeneously reducing the bit-precision level for the activations and weights of pre-trained models to low-precision integer numbers, lowering the overall memory requirements of several widely used benchmark architectures by 6.44x – 10.36x without noticeable accuracy drop. Additionally, I explored models that could automatically learn the quantization level of every weight and activation tensor at training time, directly fulfilling the memory constraints of a particular target device while learning model parameters to classify images in 51.69 ms.
I have continually demonstrated a record of research and publishing excellence, including the

collaborative publication of several articles in top-tier academic journals with over 465 citations

 

Doctor of Philosophy

Computer Engineering. King Fahd University of Petroleum and Minerals, Dhahran - Saudi Arabia. September 18, 2016 - January 05, 2023. Excellent (GPA: 3.69/4.0)

Learn More12/29/2022
 

Master of Science

Computer Engineering. King Fahd University of Petroleum and Minerals, Dhahran - Saudi Arabia. January 26, 2013 - May 24, 2016. Excellent (GPA: 3.72/4.0)

Learn More05/23/2016
 

Bachelor of Science

Computer Engineering. An-Najah National University, Nablus - Palestine. August 18, 2007 - May 23, 2012. Very Good (GPA: 80.3%)

Learn More05/11/2012
 

General Secondary Education

Scientific Stream. Silat Al-Harithiya Secondary School, Jenin - Palestine. August 26, 2001 - August 14, 2007. Excellent (GPA: 95.7%)

05/17/2007
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