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Muhammad Shafique
Vienna University of Technology
Embedded systemParallel computingEfficient energy useComputer scienceReal-time computing
347Publications
29H-index
3,911Citations
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Publications 346
Newest
#1Faiq Khalid (TU Wien: Vienna University of Technology)H-Index: 7
#2Syed Rafay Hasan (Tennessee Technological University)H-Index: 9
Last. Muhammad Shafique (TU Wien: Vienna University of Technology)H-Index: 29
view all 4 authors...
Abstract Timely detection of Hardware Trojans (HTs) has become a major challenge for secure integrated circuits. We present a run-time methodology for HT detection that employs a multi-parameter statistical traffic modeling of the communication channel in a given System-on-Chip (SoC), named as SIMCom. The main idea is to model the communication using multiple side-channel information like the Hurst exponent, the standard deviation of the injection distribution, and the hop distribution jointly t...
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#1Faiq Khalid (TU Wien: Vienna University of Technology)H-Index: 7
#2Syed Rafay Hasan (Tennessee Technological University)H-Index: 9
Last. Falah Awwad (TU Wien: Vienna University of Technology)
view all 4 authors...
Timely detection of Hardware Trojans (HT) has become a major challenge for secure integrated circuits. We present a run-time methodology for HT detection that employs a multi-parameter statistical traffic modeling of the communication channel in a given System-on-Chip (SoC). Towards this, it leverages the Hurst exponent, the standard deviation of the injection distribution and hop distribution jointly to accurately identify HT-based online anomalies. At design time, our methodology employs a pro...
Source
#1Syed Ali Asadullah Bukhari (National University of Sciences and Technology)H-Index: 2
#2Faiq Khalid (TU Wien: Vienna University of Technology)H-Index: 7
Last. Jorg Henkel (KIT: Karlsruhe Institute of Technology)H-Index: 44
view all 5 authors...
Dynamic thermal management (DTM) techniques are being widely used for attenuation of thermal hot spots in many-core systems. Conventionally, DTM techniques are analyzed using simulation and emulation methods, which are in-exhaustive due to their inherent limitations and cannot provide for a comprehensive comparison between DTM techniques owing to the wide range of corresponding design parameters. In order to handle the above discrepancies, we propose to use model checking, a state-space based fo...
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Spiking Neural Networks (SNNs) are gaining interest due to their event-driven processing which potentially consumes low power/energy computations in hardware platforms, while offering unsupervised learning capability due to the spike-timing-dependent plasticity (STDP) rule. However, state-of-the-art SNNs require a large memory footprint to achieve high accuracy, thereby making them difficult to be deployed on embedded systems, for instance on battery-powered mobile devices and IoT Edge nodes. To...
#1Maurizio CapraH-Index: 1
#2Beatrice BussolinoH-Index: 1
Last. Maurizio MartinaH-Index: 16
view all 6 authors...
Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial Intelligence (AI) applications. Their ability to go beyond human precision has made these networks a milestone in the history of AI. However, while on the one hand they present cutting edge performance, on the other hand they require enormous computing power. For this reason, numerous optimization techniques at the hardware and software level, and specialized architectures, have been developed to process these m...
1 CitationsSource
#1Heba KhdrH-Index: 9
#2Muhammad Shafique (TU Wien: Vienna University of Technology)H-Index: 29
Last. Jorg HenkelH-Index: 44
view all 5 authors...
Although manycore processors have plenty of cores, not all of them may run simultaneously at full speed and even some of them might need to be power-gated in order to keep the chip within safe temperature limits. Hence, a resource management technique, that allocates cores to application aiming at maximizing the system performance, will not be able to achieve its goal without taking into account the on-chip temperature and its impact on the availability of the chip's resources. However, consider...
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#1Muhammad Abdullah Hanif (TU Wien: Vienna University of Technology)H-Index: 8
#2Le-Ha Hoang (TU Wien: Vienna University of Technology)H-Index: 1
Last. Muhammad Shafique (TU Wien: Vienna University of Technology)H-Index: 29
view all 3 authors...
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#1Alberto Marchisio (TU Wien: Vienna University of Technology)H-Index: 4
#2Beatrice Bussolino (TU Wien: Vienna University of Technology)H-Index: 1
Last. Muhammad Shafique (Polytechnic University of Turin)H-Index: 29
view all 7 authors...
Recently, Capsule Networks (CapsNets) have shown improved performance compared to the traditional Convolutional Neural Networks (CNNs), by encoding and preserving spatial relationships between the detected features in a better way. This is achieved through the so-called Capsules (i.e., groups of neurons) that encode both the instantiation probability and the spatial information. However, one of the major hurdles in the wide adoption of CapsNets is their gigantic training time, which is primarily...
1 Citations
#1Hassan AliH-Index: 2
#2Faiq KhalidH-Index: 7
Last. Muhammad ShafiqueH-Index: 29
view all 7 authors...
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 allowing it to learn the data distribution based on the important edges in the input image. We validate our technique on Convolutional DNNs against the state-of-the-art attacks from the open-source Cleverhans library using the MNIST, the CIFAR-10, and the CIFAR-100 datasets. Our experimental results show that the attack su...
Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploitin...
1 Citations
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