Mostafa RIZK

Dr. Mostafa RIZK

Lebanese University, Beirut, Beyrouth, Lebanon
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Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 December 2021
Verified
Conference Paper
Closed access
Mostafa RIZKMostafa RIZK
Mostafa RIZK
FSFatima Slim
Fatima Slim
JCJamal Charara
Jamal Charara
Search and rescue missions during and after disasters require all efforts and high financial expenses. Rapid locating of wounded and lost individuals contributes in directing the rescuers and medical teams. This may increase the probability of saving human lives and plays a significant role in reducing expenses. Currently, the use of unmanned aerial vehicles (UAVs) or drones for remote surveillance and reconnaissance is becoming increasingly popular. On the other hand, emergent artificial intelligence (AI) algorithms based on convolution neural networks (CNN) reveal the ability of real-time detection. Combining the high-performance detection and classification capabilities provided by emergent AI techniques with the exploratory abilities of UAVs allows the UAVs to process the captured sequence of images and report back results in real-time. The evolution of AI-assisted UAVs enables the detection of wounded and trapped persons while flying and allows proper and fast transfer of information to ground stations to lead the rescuers and medical teams to victims' locations. In this paper, we explore augmenting UAVs with processing units executing emergent AI-based detectors. The proposed system can detect humans in real time and send the corresponding coordinates to the ground station.
13 October 2022
Verified
Conference Paper
Closed access
DHD. Heller
D. Heller
Mostafa RIZKMostafa RIZK
Mostafa RIZK
RDR. Douguet
R. Douguet
ABA. Baghdadi
A. Baghdadi
JDJ-Ph. Diguet
J-Ph. Diguet
Artificial Intelligence techniques based on convolution neural networks (CNNs) are now dominant in the field of object detection and classification. The deployment of CNNs on embedded edge devices targeting real-time inference sets a challenge due to the limited computing resources and power budgets. Several optimization techniques such as pruning, quantization and use of light neural networks enable the real-time inference but at the cost of precision degradation. However, using efficient approaches to apply the optimization techniques at training and inference stages enable high inference speed with limited degradation of detection performance. In this paper, we revisit the problem of detecting and classifying maritime objects. We investigate different versions of the You Only Look Once (YOLO), a state-of-the-art deep neural network, for real-time object detection and compare their performance for the specific application of detecting maritime objects. The trained YOLO networks are efficiently optimized targeting three recent edge devices: Nvidia Jetson Xavier AGX, AMD-Xilinx Kria KV260 Vision AI Kit, and Movidius Myriad X VPU. The proposed deployments demonstrate promising results with an inference speed of 90 FPS and a limited degradation of 2.4% in mean average precision.
6 August 2020 in IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Verified
Journal Article
Closed access
KAkhaled Alhaj ali
khaled Alhaj ali
Mostafa RIZKMostafa RIZK
Mostafa RIZK
Amer BaghdadiAmer Baghdadi
Amer Baghdadi
JDJean-Philippe Diguet
Jean-Philippe Diguet
JJJalal Jomaah
Jalal Jomaah
THTakahiro Hanyu
Takahiro Hanyu
In this article, we present a novel logic design style, namely, memristor overwrite logic (MOL), associated with an original MOL-based computational memory. MOL relies on a fully digital representation of memristor and can operate with different memristive device technologies. Its integration in memristive crossbar arrays and computational memories allows the execution of bit and vector-level primitive logic operations in two computational steps at most. Promising features and performances are demonstrated through the implementation of N-bit full addition using the proposed MOL-based computational memory.
24 April 2021 in Electronics
Verified
Journal Article
Open access
PDF file
KAkhaled Alhaj ali
khaled Alhaj ali
Mostafa RIZKMostafa RIZK
Mostafa RIZK
Amer BaghdadiAmer Baghdadi
Amer Baghdadi
JDJean-Philippe Diguet
Jean-Philippe Diguet
JJJalal Jomaah
Jalal Jomaah
A great deal of effort has recently been devoted to extending the usage of memristor technology from memory to computing. Memristor-based logic design is an emerging concept that targets efficient computing systems. Several logic families have evolved, each with different attributes. Memristor Ratioed Logic (MRL) has been recently introduced as a hybrid memristor–CMOS logic family. MRL requires an efficient design strategy that takes into consideration the implementation phase. This paper presents a novel MRL-based crossbar design: X-MRL. The proposed structure combines the density and scalability attributes of memristive crossbar arrays and the opportunity of their implementation at the top of CMOS layer. The evaluation of the proposed approach is performed through the design of an X-MRL-based full adder. The design is presented with its layout and corresponding simulation results using the Cadence Virtuoso toolset and CMOS 65nm process. The comparison with a pure CMOS implementation is promising in terms of the area, as our approach exhibits a 44.79% area reduction. Moreover, the combined Energy.Delay metric demonstrates a significant improvement (between ×5.7 and ×31) with respect to the available literature.
31 August 2021 in IEEE Open Journal of Circuits and Systems
Verified
Journal Article
Open access
hamoud Youneshamoud Younes
hamoud Younes
Ali IbrahimAli Ibrahim
Ali Ibrahim
Mostafa RIZKMostafa RIZK
Mostafa RIZK
Maurizio ValleMaurizio Valle
Maurizio Valle
K-Nearest Neighbor (kNN) is an efficient algorithm used in many applications, e.g., text categorization, data mining, and predictive analysis. Despite having a high computational complexity, kNN is a candidate for hardware acceleration since it is a parallelizable algorithm. This paper presents an efficient novel architecture and implementation for a kNN hardware accelerator targeting modern System-on-Chips (SoCs). The architecture adopts a selection-based sorter dedicated for kNN that outperforms traditional sorters in terms of hardware resources, time latency, and energy efficiency. The kNN architecture has been designed using High-Level Synthesis (HLS) and implemented on the Xilinx Zynqberry platform. Compared to similar state-of-the-art implementations, the proposed kNN provides speedups between $1.4\times $ and $875\times $ with 41% to 94% reductions in energy consumption. To further enhance the proposed architecture, algorithmic-level Approximate Computing Techniques (ACTs) have been applied. The proposed approximate kNN implementation accelerates the classification process by $2.3\times $ with an average reduced area size of 56% for a real-time tactile data processing case study. The approximate kNN consumes 69% less energy with an accuracy loss of less than 3% when compared to the proposed Exact kNN.
17 January 2021 in Electronics
Verified
Journal Article
Open access
PDF file
hamoud Youneshamoud Younes
hamoud Younes
Ali IbrahimAli Ibrahim
Ali Ibrahim
Mostafa RIZKMostafa RIZK
Mostafa RIZK
Maurizio ValleMaurizio Valle
Maurizio Valle
Approximate Computing Techniques (ACT) are promising solutions towards the achievement of reduced energy, time latency and hardware size for embedded implementations of machine learning algorithms. In this paper, we present the first FPGA implementation of an approximate tensorial Support Vector Machine (SVM) classifier with algorithmic level ACTs using High-Level Synthesis (HLS). A touch modality classification framework was adopted to validate the effectiveness of the proposed implementation. When compared to exact implementation presented in the state-of-the-art, the proposed implementation achieves a reduction in power consumption by up to 49% with a speedup of 3.2×. Moreover, the hardware resources are reduced by 40% while consuming 82% less energy in classifying an input touch with an accuracy loss less than 5%.