217 citations · 1.3k across the 49 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…
FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation
Le-Ha Hoang, Muhammad Abdullah Hanif, Muhammad Shafique
Deep Neural Networks (DNNs) are widely being adopted for safety-critical applications, e.g., healthcare and autonomous driving. Inherently, they are considered to be highly error-t…
ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations
Alberto Marchisio, Vojtech Mrazek, Muhammad Abudllah Hanif +1
Recent advances in Capsule Networks (CapsNets) have shown their superior learning capability, compared to the traditional Convolutional Neural Networks (CNNs). However, the extreme…
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks
Mahum Naseer, Mishal Fatima Minhas, Faiq Khalid +3
With a constant improvement in the network architectures and training methodologies, Neural Networks (NNs) are increasingly being deployed in real-world Machine Learning systems. H…
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks
Alberto Marchisio, Beatrice Bussolino, Alessio Colucci +4
Recently, Capsule Networks (CapsNets) have shown improved performance compared to the traditional Convolutional Neural Networks (CNNs), by encoding and preserving spatial relations…
CapStore: Energy-Efficient Design and Management of the On-Chip Memory for CapsuleNet Inference Accelerators
Alberto Marchisio, Muhammad Abdullah Hanif, Mohammad Taghi Teimoori +1
Deep Neural Networks (DNNs) have been established as the state-of-the-art algorithm for advanced machine learning applications. Recently, CapsuleNets have improved the generalizati…
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks
Alberto Marchisio, Giorgio Nanfa, Faiq Khalid +3
Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent w…
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks
Alberto Marchisio, Giorgio Nanfa, Faiq Khalid +3
Capsule Networks preserve the hierarchical spatial relationships between objects, and thereby bears a potential to surpass the performance of traditional Convolutional Neural Netwo…
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning
Faiq Khalid, Muhammmad Abdullah Hanif, Semeen Rehman +2
Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior…
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Faiq Khalid, Hassan Ali, Hammad Tariq +4
Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs). In this paper, we propose two quan…
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks
Faiq Khalid, Muhammad Abdullah Hanif, Semeen Rehman +2
Most of the data manipulation attacks on deep neural networks (DNNs) during the training stage introduce a perceptible noise that can be catered by preprocessing during inference o…