217 citations · 1.3k across the 49 of their papers we have counts for
49 papers
GAP: Game Theory-Based Approach for Reliability and Power Management in Emerging Fog Computing
Abolfazl Younesi, Mohsen Ansari, Alireza Ejlali +3
Fog computing brings about a transformative shift in data management, presenting unprecedented opportunities for enhanced performance and reduced latency. However, one of the key a…
ISimDL: Importance Sampling-Driven Acceleration of Fault Injection Simulations for Evaluating the Robustness of Deep Learning
Alessio Colucci, Andreas Steininger, Muhammad Shafique
Deep Learning (DL) systems have proliferated in many applications, requiring specialized hardware accelerators and chips. In the nano-era, devices have become increasingly more sus…
Xel-FPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems
Bharath Srinivas Prabakaran, Vojtech Mrazek, Zdenek Vasicek +2
Generation and exploration of approximate circuits and accelerators has been a prominent research domain to achieve energy-efficiency and/or performance improvements. This research…
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…
Side-Channel Attacks on RISC-V Processors: Current Progress, Challenges, and Opportunities
Mahya Morid Ahmadi, Faiq Khalid, Muhammad Shafique
Side-channel attacks on microprocessors, like the RISC-V, exhibit security vulnerabilities that lead to several design challenges. Hence, it is imperative to study and analyze thes…
Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks
Faiq Khalid, Muhammad Abdullah Hanif, Muhammad Shafique
From tiny pacemaker chips to aircraft collision avoidance systems, the state-of-the-art Cyber-Physical Systems (CPS) have increasingly started to rely on Deep Neural Networks (DNNs…
SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Spiking Neural Networks (SNNs) bear the potential of efficient unsupervised and continual learning capabilities because of their biological plausibility, but their complexity still…
SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM
Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
Spiking Neural Networks (SNNs) have the potential for achieving low energy consumption due to their biologically sparse computation. Several studies have shown that the off-chip me…
DNN-Life: An Energy-Efficient Aging Mitigation Framework for Improving the Lifetime of On-Chip Weight Memories in Deep Neural Network Hardware Architectures
Muhammad Abdullah Hanif, Muhammad Shafique
Negative Biased Temperature Instability (NBTI)-induced aging is one of the critical reliability threats in nano-scale devices. This paper makes the first attempt to study the NBTI…
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead
Muhammad Shafique, Mahum Naseer, Theocharis Theocharides +4
Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities. Ho…
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead
Maurizio Capra, Beatrice Bussolino, Alberto Marchisio +3
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to au…
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…
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking
Lilas Alrahis, Satwik Patnaik, Faiq Khalid +4
In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less machine learning-based attack on provably secure logic locking that can identify any desired protection logic…
Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters
Rida El-Allami, Alberto Marchisio, Muhammad Shafique +1
Deep Learning (DL) algorithms have gained popularity owing to their practical problem-solving capacity. However, they suffer from a serious integrity threat, i.e., their vulnerabil…
MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Resource-Constrained IoT Edge Devices
Faiq Khalid, Syed Rafay Hasan, Sara Zia +3
Traditional learning-based approaches for run-time Hardware Trojan detection require complex and expensive on-chip data acquisition frameworks and thus incur high area and power ov…
DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware
Alberto Marchisio, Vojtech Mrazek, Muhammad Abdullah Hanif +1
Deep Neural Networks (DNNs) have been established as the state-of-the-art algorithm for advanced machine learning applications. Recently proposed by the Google Brain's team, the Ca…
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks
Alberto Marchisio, Andrea Massa, Vojtech Mrazek +3
Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Go…
FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Spiking Neural Networks (SNNs) are gaining interest due to their event-driven processing which potentially consumes low power/energy computations in hardware platforms, while offer…
An Efficient Spiking Neural Network for Recognizing Gestures with a DVS Camera on the Loihi Neuromorphic Processor
Riccardo Massa, Alberto Marchisio, Maurizio Martina +1
Spiking Neural Networks (SNNs), the third generation NNs, have come under the spotlight for machine learning based applications due to their biological plausibility and reduced com…
NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips
Valerio Venceslai, Alberto Marchisio, Ihsen Alouani +2
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…
ApproxFPGAs: Embracing ASIC-Based Approximate Arithmetic Components for FPGA-Based Systems
Bharath Srinivas Prabakaran, Vojtech Mrazek, Zdenek Vasicek +2
There has been abundant research on the development of Approximate Circuits (ACs) for ASICs. However, previous studies have illustrated that ASIC-based ACs offer asymmetrical gains…
EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction
Bharath Srinivas Prabakaran, Alberto García Jiménez, Germán Moltó Martínez +1
State-of-the-art techniques for detecting, or predicting, neurological disorders (1) focus on predicting each disorder individually, and are (2) computationally expensive, leading…
DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks
Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
Many convolutional neural network (CNN) accelerators face performance- and energy-efficiency challenges which are crucial for embedded implementations, due to high DRAM access late…
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks
Alberto Marchisio, Beatrice Bussolino, Alessio Colucci +3
Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the t…