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

22 citations · 29 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CR20235 cited

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

Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni +1

In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the conc…

cs.AR2023

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

Muhammad Abdullah Hanif, Muhammad Shafique

Fault-aware retraining has emerged as a prominent technique for mitigating permanent faults in Deep Neural Network (DNN) hardware accelerators. However, retraining leads to huge ov…

cs.AR2023

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

Muhammad Abdullah Hanif, Muhammad Shafique

Permanent faults induced due to imperfections in the manufacturing process of Deep Neural Network (DNN) accelerators are a major concern, as they negatively impact the manufacturin…

cs.AR20231 cited

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

Muhammad Abdullah Hanif, Muhammad Shafique

Fault-Aware Training (FAT) has emerged as a highly effective technique for addressing permanent faults in DNN accelerators, as it offers fault mitigation without significant perfor…

cs.NE202322 cited

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

Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique

Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under unsupervised settings and low operational power/energy due to their bio-plausible computatio…

cs.CR2023

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

Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif +2

Graph neural networks (GNNs) have shown great success in detecting intellectual property (IP) piracy and hardware Trojans (HTs). However, the machine learning community has demonst…