49 citations · 158 across the 19 of their papers we have counts for
21 papers
Quantum Graph Transformer for NLP Sentiment Classification
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +1
Quantum machine learning is a promising direction for building more efficient and expressive models, particularly in domains where understanding complex, structured data is critica…
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Shamminuj Aktar, Andreas Bärtschi, Diane Oyen +2
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs…
LLVM Static Analysis for Program Characterization and Memory Reuse Profile Estimation
Atanu Barai, Nandakishore Santhi, Abdur Razzak +2
Profiling various application characteristics, including the number of different arithmetic operations performed, memory footprint, etc., dynamically is time- and space-consuming.…
Predicting Expressibility of Parameterized Quantum Circuits using Graph Neural Network
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +2
Parameterized Quantum Circuits (PQCs) are essential to quantum machine learning and optimization algorithms. The expressibility of PQCs, which measures their ability to represent a…
Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection
Amin Sarihi, Ahmad Patooghy, Peter Jamieson +1
Current Hardware Trojan (HT) detection techniques are mostly developed based on a limited set of HT benchmarks. Existing HT benchmark circuits are generated with multiple shortcomi…
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach
Amin Sarihi, Peter Jamieson, Ahmad Patooghy +1
Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits. HTs can be inserte…
Scalable Experimental Bounds for Entangled Quantum State Fidelities
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +1
Estimating the state preparation fidelity of highly entangled states on noisy intermediate-scale quantum (NISQ) devices is important for benchmarking and application considerations…
Quantum Netlist Compiler (QNC)
Shamminuj Aktar, Abdel-Hameed A. Badawy, Nandakishore Santhi
Over the last decade, Quantum Computing hardware has rapidly developed and become a very intriguing, promising, and active research field among scientists worldwide. To achieve the…
Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis
Hamdy Abdelkhalik, Yehia Arafa, Nandakishore Santhi +1
Graphics processing units (GPUs) are now considered the leading hardware to accelerate general-purpose workloads such as AI, data analytics, and HPC. Over the last decade, research…
Hardware Trojan Insertion Using Reinforcement Learning
Amin Sarihi, Ahmad Patooghy, Peter Jamieson +1
This paper utilizes Reinforcement Learning (RL) as a means to automate the Hardware Trojan (HT) insertion process to eliminate the inherent human biases that limit the development…
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques
Hamdy Abdelkhalik, Shamminuj Aktar, Yehia Arafa +8
Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to…
A Divide-and-Conquer Approach to Dicke State Preparation
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +1
We present a divide-and-conquer approach to deterministically prepare Dicke states (i.e., equal-weight superpositions of all -qubit states with Hamming Wei…
PPT-Multicore: Performance Prediction of OpenMP applications using Reuse Profiles and Analytical Modeling
Atanu Barai, Yehia Arafa, Abdel-Hameed Badawy +3
We present PPT-Multicore, an analytical model embedded in the Performance Prediction Toolkit (PPT) to predict parallel application performance running on a multicore processor. PPT…
PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace
Atanu Barai, Gopinath Chennupati, Nandakishore Santhi +3
Performance modeling of parallel applications on multicore processors remains a challenge in computational co-design due to multicore processors' complex design. Multicores include…
Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs
Yehia Arafa, Ammar ElWazir, Abdelrahman ElKanishy +6
GPUs are prevalent in modern computing systems at all scales. They consume a significant fraction of the energy in these systems. However, vendors do not publish the actual cost of…
Modeling Shared Cache Performance of OpenMP Programs using Reuse Distance
Atanu Barai, Gopinath Chennupati, Nandakishore Santhi +2
Performance modeling of parallel applications on multicore computers remains a challenge in computational co-design due to the complex design of multicore processors including priv…
Low Overhead Instruction Latency Characterization for NVIDIA GPGPUs
Yehia Arafa, Abdel-Hameed Badawy, Gopinath Chennupati +2
The last decade has seen a shift in the computer systems industry where heterogeneous computing has become prevalent. Graphics Processing Units (GPUs) are now present in supercompu…
Energy Efficient Tri-State CNFET Ternary Logic Gates
Sepher Tabrizchi, Fazel Sharifi, Abdel-Hameed A. Badawy
Traditional silicon binary circuits continue to face challenges such as high leakage power dissipation and large area of interconnections. Multiple-Valued Logic (MVL) and nano devi…
Design of Adiabatic MTJ-CMOS Hybrid Circuits
Fazel Sharifi, Z. M. Saifullah, Abdel-Hameed Badawy
Low-power designs are a necessity with the increasing demand of portable devices which are battery operated. In many of such devices the operational speed is not as important as ba…
DyAdHyTM: A Low Overhead Dynamically Adaptive Hybrid Transactional Memory on Big Data Graphs
Mohammad Qayum, Abdel-Hameed Badawy, Jeanine Cook
Big data is a buzzword used to describe massive volumes of data that provides opportunities of exploring new insights through data analytics. However, big data is mostly structured…
MorphoNoC: Exploring the Design Space of a Configurable Hybrid NoC using Nanophotonics
Vikram K. Narayana, Shuai Sun, Abdel-Hameed A. Badawy +2
As diminishing feature sizes drive down the energy for computations, the power budget for on-chip communication is steadily rising. Furthermore, the increasing number of cores is p…