Muhammad Abdullah Hanif

research

∙ 08/11/2023

Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

In this paper, we present a comprehensive survey of the current trends f...

0 Amira Guesmi, et al. ∙

research

∙ 08/06/2023

SAAM: Stealthy Adversarial Attack on Monoculor Depth Estimation

In this paper, we investigate the vulnerability of MDE to adversarial pa...

0 Amira Guesmi, et al. ∙

research

∙ 07/20/2023

Approximate Computing Survey, Part II: Application-Specific Architectural Approximation Techniques and Applications

The challenging deployment of compute-intensive applications from domain...

0 Vasileios Leon, et al. ∙

research

∙ 07/20/2023

Approximate Computing Survey, Part I: Terminology and Software Hardware Approximation Techniques

The rapid growth of demanding applications in domains applying multimedi...

0 Vasileios Leon, et al. ∙

research

∙ 05/21/2023

Reduce: A Framework for Reducing the Overheads of Fault-Aware Retraining

Fault-aware retraining has emerged as a prominent technique for mitigati...

0 Muhammad Abdullah Hanif, et al. ∙

research

∙ 05/21/2023

FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization

Permanent faults induced due to imperfections in the manufacturing proce...

0 Muhammad Abdullah Hanif, et al. ∙

research

∙ 05/19/2023

DAP: A Dynamic Adversarial Patch for Evading Person Detectors

In this paper, we present a novel approach for generating naturalistic a...

0 Amira Guesmi, et al. ∙

research

∙ 04/20/2023

eFAT: Improving the Effectiveness of Fault-Aware Training for Mitigating Permanent Faults in DNN Hardware Accelerators

Fault-Aware Training (FAT) has emerged as a highly effective technique f...

0 Muhammad Abdullah Hanif, et al. ∙

research

∙ 04/08/2023

RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults

To maximize the performance and energy efficiency of Spiking Neural Netw...

0 Rachmad Vidya Wicaksana Putra, et al. ∙

research

∙ 04/08/2023

EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems

Spiking Neural Networks (SNNs) have shown capabilities of achieving high...

0 Rachmad Vidya Wicaksana Putra, et al. ∙

research

∙ 03/24/2023

PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems

Graph neural networks (GNNs) have shown great success in detecting intel...

0 Lilas Alrahis, et al. ∙

research

∙ 03/04/2023

scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation

Approximate computing (AC) has become a prominent solution to improve th...

0 Ebrahim Farahmand, et al. ∙

research

∙ 03/03/2023

Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens

Machine Learning (ML) architectures have been applied to several applica...

0 Ayoub Arous, et al. ∙

research

∙ 03/02/2023

APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation

In recent years, monocular depth estimation (MDE) has witnessed a substa...

11 Amira Guesmi, et al. ∙

research

∙ 03/02/2023

AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems

Vision-based perception modules are increasingly deployed in many applic...

0 Amira Guesmi, et al. ∙

research

∙ 07/31/2022

CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks

In today's era of smart cyber-physical systems, Deep Neural Networks (DN...

0 Muhammad Abdullah Hanif, et al. ∙

research

∙ 04/18/2022

Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems

The real-world use cases of Machine Learning (ML) have exploded over the...

22 Shail Dave, et al. ∙

research

∙ 03/10/2022

SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network Accelerators under Soft Errors

Specialized hardware accelerators have been designed and employed to max...

0 Rachmad Vidya Wicaksana Putra, et al. ∙

research

∙ 11/13/2021

UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction

Logic locking aims to prevent intellectual property (IP) piracy and unau...

0 Lilas Alrahis, et al. ∙

research

∙ 09/20/2021

Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework

The security and privacy concerns along with the amount of data that is ...

7 Muhammad Shafique, et al. ∙

research

∙ 08/23/2021

ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories

Spiking neural networks (SNNs) have shown a potential for having low ene...

0 Rachmad Vidya Wicaksana Putra, et al. ∙

research

∙ 06/16/2021

High Performance and Optimal Configuration of Accurate Heterogeneous Block-Based Approximate Adder

Approximate computing is an emerging paradigm to improve power and perfo...

0 Ebrahim Farahmand, et al. ∙

research

∙ 05/26/2021

Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks

Continual learning is essential for all real-world applications, as froz...

0 Khadija Shaheen, et al. ∙

research

∙ 05/05/2021

Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks

From tiny pacemaker chips to aircraft collision avoidance systems, the s...

8 Faiq Khalid, et al. ∙

research

∙ 02/28/2021

SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM

Spiking Neural Networks (SNNs) have the potential for achieving low ener...

0 Rachmad Vidya Wicaksana Putra, et al. ∙

research

∙ 01/29/2021

DNN-Life: An Energy-Efficient Aging Mitigation Framework for Improving the Lifetime of On-Chip Weight Memories in Deep Neural Network Hardware Architectures

Negative Biased Temperature Instability (NBTI)-induced aging is one of t...

0 Muhammad Abdullah Hanif, et al. ∙

research

∙ 12/10/2020

GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking

In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less m...

0 Lilas Alrahis, et al. ∙

research

∙ 10/12/2020

DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware

Deep Neural Networks (DNNs) have been established as the state-of-the-ar...

0 Alberto Marchisio, et al. ∙

research

∙ 04/21/2020

DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks

Many convolutional neural network (CNN) accelerators face performance- a...

0 Rachmad Vidya Wicaksana Putra, et al. ∙

research

∙ 12/03/2019

FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks

With a constant improvement in the network architectures and training me...

0 Mahum Naseer, et al. ∙

research

∙ 12/02/2019

FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation

Deep Neural Networks (DNNs) are widely being adopted for safety-critical...

0 Le-Ha Hoang, et al. ∙

research

∙ 06/11/2019

ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining

The state-of-the-art approaches employ approximate computing to improve ...

0 Vojtech Mrazek, et al. ∙

research

∙ 05/24/2019

X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations

Convolutional Neural Networks (CNNs) are extensively in use due to their...

0 Alberto Marchisio, et al. ∙

research

∙ 02/22/2019

autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components

Approximate computing is an emerging paradigm for developing highly ener...

0 Vojtech Mrazek, et al. ∙

research

∙ 02/04/2019

ROMANet: Fine-Grained Reuse-Driven Data Organization and Off-Chip Memory Access Management for Deep Neural Network Accelerators

Many hardware accelerators have been proposed to improve the computation...

0 Rachmad Vidya Wicaksana Putra, et al. ∙

research

∙ 02/04/2019

SNN under Attack: are Spiking Deep Belief Networks vulnerable to Adversarial Examples?

Recently, many adversarial examples have emerged for Deep Neural Network...

16 Alberto Marchisio, et al. ∙

research

∙ 01/29/2019

RED-Attack: Resource Efficient Decision based Attack for Machine Learning

Due to data dependency and model leakage properties, Deep Neural Network...

0 Faiq Khalid, et al. ∙

research

∙ 01/28/2019

CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks

Capsule Networks envision an innovative point of view about the represen...

0 Alberto Marchisio, et al. ∙

research

∙ 11/05/2018

Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference

The exponential increase in dependencies between the cyber and physical ...

0 Faiq Khalid, et al. ∙

research

∙ 11/04/2018

SSCNets: A Selective Sobel Convolution-based Technique to Enhance the Robustness of Deep Neural Networks against Security Attacks

Recent studies have shown that slight perturbations in the input data ca...

0 Hammad Tariq, et al. ∙

research

∙ 11/04/2018

QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks

Deep Neural Networks (DNNs) have recently been shown vulnerable to adver...

0 Hassan Ali, et al. ∙

research

∙ 11/02/2018

ISA4ML: Training Data-Unaware Imperceptible Security Attacks on Machine Learning Modules of Autonomous Vehicles

Due to big data analysis ability, machine learning (ML) algorithms are b...

0 Faiq Khalid, et al. ∙

research

∙ 11/02/2018

CapsAcc: An Efficient Hardware Accelerator for CapsuleNets with Data Reuse

Deep Neural Networks (DNNs) have been widely deployed for many Machine L...

0 Alberto Marchisio, et al. ∙

research

∙ 10/30/2018

MPNA: A Massively-Parallel Neural Array Accelerator with Dataflow Optimization for Convolutional Neural Networks

The state-of-the-art accelerators for Convolutional Neural Networks (CNN...

0 Muhammad Abdullah Hanif, et al. ∙

research

∙ 10/27/2018

A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks

Activation functions influence behavior and performance of DNNs. Nonline...

0 Alberto Marchisio, et al. ∙

Muhammad Abdullah Hanif

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