50 citations · 313 across the 50 of their papers we have counts for
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MuxLink: Circumventing Learning-Resilient MUX-Locking Using Graph Neural Network-based Link Prediction
Lilas Alrahis, Satwik Patnaik, Muhammad Shafique +1
Logic locking has received considerable interest as a prominent technique for protecting the design intellectual property from untrusted entities, especially the foundry. Recently,…
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction
Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif +2
Logic locking aims to prevent intellectual property (IP) piracy and unauthorized overproduction of integrated circuits (ICs). However, initial logic locking techniques were vulnera…
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework
Muhammad Shafique, Alberto Marchisio, Rachmad Vidya Wicaksana Putra +1
The security and privacy concerns along with the amount of data that is required to be processed on regular basis has pushed processing to the edge of the computing systems. Deploy…
R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors
Alberto Marchisio, Giacomo Pira, Maurizio Martina +2
Spiking Neural Networks (SNNs) aim at providing energy-efficient learning capabilities when implemented on neuromorphic chips with event-based Dynamic Vision Sensors (DVS). This pa…
ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories
Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
Spiking neural networks (SNNs) have shown a potential for having low energy with unsupervised learning capabilities due to their biologically-inspired computation. However, they ma…
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks
Alberto Marchisio, Giacomo Pira, Maurizio Martina +2
Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are vulnerable t…
CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor
Alberto Viale, Alberto Marchisio, Maurizio Martina +2
Autonomous Driving (AD) related features provide new forms of mobility that are also beneficial for other kind of intelligent and autonomous systems like robots, smart transportati…
Design and Analysis of High Performance Heterogeneous Block-based Approximate Adders
Ebrahim Farahmand, Ali Mahani, Muhammad Abdullah Hanif +1
Approximate computing is an emerging paradigm to improve the power and performance efficiency of error-resilient applications. As adders are one of the key components in almost all…
Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks
Khadija Shaheen, Muhammad Abdullah Hanif, Osman Hasan +1
Continual learning is essential for all real-world applications, as frozen pre-trained models cannot effectively deal with non-stationary data distributions. The purpose of this st…