217 citations · 1.3k across the 49 of their papers we have counts for
13 papers · 1 filter
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…
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
Vojtech Mrazek, Zdenek Vasicek, Lukas Sekanina +2
The state-of-the-art approaches employ approximate computing to reduce the energy consumption of DNN hardware. Approximate DNNs then require extensive retraining afterwards to reco…
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…
autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components
Vojtech Mrazek, Muhammad Abdullah Hanif, Zdenek Vasicek +2
Approximate computing is an emerging paradigm for developing highly energy-efficient computing systems such as various accelerators. In the literature, many libraries of elementary…
Systimator: A Design Space Exploration Methodology for Systolic Array based CNNs Acceleration on the FPGA-based Edge Nodes
Hazoor Ahmad, Muhammad Tanvir, Muhammad Abdullah Hanif +3
The evolution of IoT based smart applications demand porting of artificial intelligence algorithms to the edge computing devices. CNNs form a large part of these AI algorithms. Sys…
XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge
Bharath Srinivas Prabakaran, Semeen Rehman, Muhammad Shafique
Bio-signals exhibit high redundancy, and the algorithms for their processing are inherently error resilient. This property can be leveraged to improve the energy-efficiency of IoT-…
ROMANet: Fine-Grained Reuse-Driven Off-Chip Memory Access Management and Data Organization for Deep Neural Network Accelerators
Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
Enabling high energy efficiency is crucial for embedded implementations of deep learning. Several studies have shown that the DRAM-based off-chip memory accesses are one of the mos…
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…
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
Faiq Khalid, Hassan Ali, Muhammad Abdullah Hanif +3
Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them…
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…