15 citations · 35 across the 27 of their papers we have counts for
29 papers
EdgeCIM: A Hardware-Software Co-Design for CIM-Based Acceleration of Small Language Models
Jinane Bazzi, Mariam Rakka, Fadi Kurdahi +2
The growing demand for deploying Small Language Models (SLMs) on edge devices, including laptops, smartphones, and embedded platforms, has exposed fundamental inefficiencies in exi…
Mixed-Precision Quantization for Language Models: Techniques and Prospects
Mariam Rakka, Marios Fournarakis, Olga Krestinskaya +5
The rapid scaling of language models (LMs) has resulted in unprecedented computational, memory, and energy requirements, making their training and deployment increasingly unsustain…
CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1
To maximize hardware efficiency and performance accuracy in Compute-In-Memory (CIM)-based neural network accelerators for Artificial Intelligence (AI) applications, co-optimizing b…
On Jailbreaking Quantized Language Models Through Fault Injection Attacks
Noureldin Zahran, Ahmad Tahmasivand, Ihsen Alouani +2
The safety alignment of Language Models (LMs) is a critical concern, yet their integrity can be challenged by direct parameter manipulation attacks, such as those potentially induc…
Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection
Diego A. Silva, Ahmed Elsheikh, Kamilya Smagulova +2
Event-based cameras are sensors that simulate the human eye, offering advantages such as high-speed robustness and low power consumption. Established Deep Learning techniques have…
SoftmAP: Software-Hardware Co-design for Integer-Only Softmax on Associative Processors
Mariam Rakka, Jinhao Li, Guohao Dai +3
Recent research efforts focus on reducing the computational and memory overheads of Large Language Models (LLMs) to make them feasible on resource-constrained devices. Despite adva…
BF-IMNA: A Bit Fluid In-Memory Neural Architecture for Neural Network Acceleration
Mariam Rakka, Rachid Karami, Ahmed M. Eltawil +2
Mixed-precision quantization works Neural Networks (NNs) are gaining traction for their efficient realization on the hardware leading to higher throughput and lower energy. In-Memo…
Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimization
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1
Designing generalized in-memory computing (IMC) hardware that efficiently supports a variety of workloads requires extensive design space exploration, which is infeasible to perfor…
A Recurrent YOLOv8-based framework for Event-Based Object Detection
Diego A. Silva, Kamilya Smagulova, Ahmed Elsheikh +2
Object detection is crucial in various cutting-edge applications, such as autonomous vehicles and advanced robotics systems, primarily relying on data from conventional frame-based…
AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition
Mohamad Fakih, Rouwaida Kanj, Fadi Kurdahi +1
Automatic Speech Recognition systems have been shown to be vulnerable to adversarial attacks that manipulate the command executed on the device. Recent research has focused on expl…
Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function
Ayan Shymyrbay, Mohammed E. Fouda, Ahmed Eltawil
Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable…
Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions
Kamilya Smagulova, Mohammed E. Fouda, Ahmed Eltawil
The higher speed, scalability and parallelism offered by ReRAM crossbar arrays foster development of ReRAM-based next generation AI accelerators. At the same time, sensitivity of R…
Mixed-Precision Neural Networks: A Survey
Mariam Rakka, Mohammed E. Fouda, Pramod Khargonekar +1
Mixed-precision Deep Neural Networks achieve the energy efficiency and throughput needed for hardware deployment, particularly when the resources are limited, without sacrificing a…
DNA Pattern Matching Acceleration with Analog Resistive CAM
Jinane Bazzi, Jana Sweidan, Mohammed E. Fouda +2
DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications, since analyzing changes in DNA sequences can inc…
BackLink: Supervised Local Training with Backward Links
Wenzhe Guo, Mohammed E Fouda, Ahmed M. Eltawil +1
Empowered by the backpropagation (BP) algorithm, deep neural networks have dominated the race in solving various cognitive tasks. The restricted training pattern in the standard BP…
DT2CAM: A Decision Tree to Content Addressable Memory Framework
Mariam Rakka, Mohammed E. Fouda, Rouwaida Kanj +1
Decision trees are considered one of the most powerful tools for data classification. Accelerating the decision tree search is crucial for on-the-edge applications that have limite…
In-memory Associative Processors: Tutorial, Potential, and Challenges
Mohammed E. Fouda, Hasan Erdem Yantir, Ahmed M. Eltawil +1
In-memory computing is an emerging computing paradigm that overcomes the limitations of exiting Von-Neumann computing architectures such as the memory-wall bottleneck. In such para…
Efficient Analog CAM Design
Jinane Bazzi, Jana Sweidan, Mohammed E. Fouda +2
Content Addressable Memories (CAMs) are considered a key-enabler for in-memory computing (IMC). IMC shows order of magnitude improvement in energy efficiency and throughput compare…
Configurable Independent Component Analysis Preprocessing Accelerator
Hsi-Hung Lu, Chung-An Shen, Mohammed E. Fouda +1
Independent component analysis (ICA) has been used in many applications, including self-interference cancellation for in-band full-duplex wireless systems and anomaly detection in…
Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Time
Wenzhe Guo, Mohammed E. Fouda, Ahmed M. Eltawil +1
Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known bac…
CAPTIVE: Constrained Adversarial Perturbations to Thwart IC Reverse Engineering
Amir Hosein Afandizadeh Zargari, Marzieh AshrafiAmiri, Minjun Seo +3
Reverse engineering (RE) in Integrated Circuits (IC) is a process in which one will attempt to extract the internals of an IC, extract the circuit structure, and determine the gate…
In-memory Multi-valued Associative Processor
Mira Hout, Mohammed E. Fouda, Rouwaida Kanj +1
In-memory associative processor architectures are offered as a great candidate to overcome memory-wall bottleneck and to enable vector/parallel arithmetic operations. In this paper…
Efficient Noise Mitigation Technique for Quantum Computing
Ali Shaib, Mohamad H. Naim, Mohammed E. Fouda +2
Quantum computers have enabled solving problems beyond the current computers' capabilities. However, this requires handling noise arising from unwanted interactions in these system…
Resistive Neural Hardware Accelerators
Kamilya Smagulova, Mohammed E. Fouda, Fadi Kurdahi +2
Deep Neural Networks (DNNs), as a subset of Machine Learning (ML) techniques, entail that real-world data can be learned and that decisions can be made in real-time. However, their…