165 citations · 330 across the 20 of their papers we have counts for
22 papers
AMAX: Accuracy-Aware Multi-Loop Asymmetric Perforation for Heterogeneous Architectures
Amna Hassan, Paul Danielse, Anuj Pathania +2
Thermal constraints in heterogeneous multi-core systems can significantly degrade performance, as conventional techniques such as dynamic voltage and frequency scaling (DVFS) and t…
UniCASE: A Unified 16-bit Floating-Point Format with Criticality-Aware Selective ECC for Efficient DNN Protection
Amna Hassan, Semeen Rehman
Soft errors are an increasing reliability concern for Deep Neural Network execution because they can corrupt parameters, leading to accuracy degradation. While conventional ECC off…
dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network
Asad Aftab, Semeen Rehman
Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge s…
Holo-Block Chain: A Hybrid Approach for Secured IoT Healthcare Ecosystem
Asad Aftab, Chrysostomos Chrysostomou, Hassaan Khaliq Qureshi +1
The Internet-of-Things (IoT) is an imminent and corporal technology that enables the connectivity of smart physical devices with virtual objects contriving in distinct platforms wi…
Reliable and Resilient AI and IoT-based Personalised Healthcare Services: A Survey
Najma Taimoor, Semeen Rehman
Recent technological and economic developments have transformed the healthcare sector towards more personalized and IoT-based healthcare services. These services are realized throu…
EXTENT: Enabling Approximation-Oriented Energy Efficient STT-RAM Write Circuit
Saeed Seyedfaraji, Javad Talafy Daryani, Mohamed M. Sabry Aly +1
Spin Transfer Torque Random Access Memory (STT-RAM) has garnered interest due to its various characteristics such as non-volatility, low leakage power, high density. Its magnetic p…
SMART: Investigating the Impact of Threshold Voltage Suppression in an In-SRAM Multiplication/Accumulation Accelerator for Accuracy Improvement in 65 nm CMOS Technology
Saeed Seyedfaraji, Baset Mesgari, Semeen Rehman
State-of-the-art in-memory computation has recently emerged as the most promising solution to overcome design challenges related to data movement inside current computing systems.…
On the Validation of Multi-Level Personalised Health Condition Model
Najma Taimoor, Semeen Rehman
This paper presents a verification-based methodology to validate the model of personalized health conditions. The model identifies the values that may result in unsafe, un-reachabl…
AID: Accuracy Improvement of Analog Discharge-Based in-SRAM Multiplication Accelerator
Saeed Seyedfaraji, Baset Mesgari, Semeen Rehman
This paper presents a novel circuit (AID) to improve the accuracy of an energy-efficient in-memory multiplier using a standard 6T-SRAM. The state-of-the-art discharge-based in-SRAM…
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables
Bharath Srinivas Prabakaran, Asima Akhtar, Semeen Rehman +2
In this work, we propose the BioNetExplorer framework to systematically generate and explore multiple DNN architectures for bio-signal processing in wearables. Our framework adapts…
MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences
Alessio Colucci, Dávid Juhász, Martin Mosbeck +6
Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjec…
XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge
Bharath Srinivas Prabakaran, Semeen Rehman, Muhammad Shafique
Bio-signals exhibit high redundancy, and the algorithms for their processing are inherently error resilient. This property can be leveraged to improve the energy-efficiency of IoT-…
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
Faiq Khalid, Hassan Ali, Muhammad Abdullah Hanif +3
Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them…
ForASec: Formal Analysis of Security Vulnerabilities in Sequential Circuits
Faiq Khalid, Imran Hafeez Abbassi, Semeen Rehman +3
Security vulnerability analysis of Integrated Circuits using conventional design-time validation and verification techniques (like simulations, emulations, etc.) is generally a com…
TrojanZero: Switching Activity-Aware Design of Undetectable Hardware Trojans with Zero Power and Area Footprint
Imran Hafeez Abbassi, Faiq Khalid, Semeen Rehman +4
Conventional Hardware Trojan (HT) detection techniques are based on the validation of integrated circuits to determine changes in their functionality, and on non-invasive side-chan…
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning
Faiq Khalid, Muhammmad Abdullah Hanif, Semeen Rehman +2
Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior…
Architectural-Space Exploration of Heterogeneous Reliability and Checkpointing Modes for Out-of-Order Superscalar Processors
Bharath Srinivas Prabakaran, Mihika Dave, Florian Kriebel +2
Reliability has emerged as a key topic of interest for researchers around the world to detect and/or mitigate the side effects of decreasing transistor sizes, such as soft errors.…
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
Hassan Ali, Faiq Khalid, Hammad Tariq +4
In this paper, we introduce a novel technique based on the Secure Selective Convolutional (SSC) techniques in the training loop that increases the robustness of a given DNN by allo…
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Faiq Khalid, Hassan Ali, Hammad Tariq +4
Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs). In this paper, we propose two quan…
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks
Faiq Khalid, Muhammad Abdullah Hanif, Semeen Rehman +2
Most of the data manipulation attacks on deep neural networks (DNNs) during the training stage introduce a perceptible noise that can be catered by preprocessing during inference o…
MPNA: A Massively-Parallel Neural Array Accelerator with Dataflow Optimization for Convolutional Neural Networks
Muhammad Abdullah Hanif, Rachmad Vidya Wicaksana Putra, Muhammad Tanvir +3
The state-of-the-art accelerators for Convolutional Neural Networks (CNNs) typically focus on accelerating only the convolutional layers, but do not prioritize the fully-connected…
A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks
Alberto Marchisio, Muhammad Abdullah Hanif, Semeen Rehman +2
Activation functions influence behavior and performance of DNNs. Nonlinear activation functions, like Rectified Linear Units (ReLU), Exponential Linear Units (ELU) and Scaled Expon…