17 citations · 58 across the 29 of their papers we have counts for
34 papers
Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge
Kyrylo Nazarevych, Mohammad Hasan Ahmadilivani, Krister Kaldre +2
Convolutional Neural Networks (CNNs) are increasingly deployed in safety-critical edge applications, where soft errors can silently corrupt inference outputs and lead to unsafe dec…
ANTMAN: An Efficient and Interpretable RTL-Level Run-Time Detection Framework for Stealthy Branch Predictor Attacks on BOOM
Muhammad Hassan, Maria Mushtaq, Sharjeel Imtiaz +2
Runtime detection of microarchitectural side channel attacks remains significantly underexplored in RISCV compared with x86 and ARM ISAs, posing a serious threat to critical applic…
CheckOne: Lightweight Fault Detection and Mitigation for Vision Transformers
Mohammad Hasan Ahmadilivani, Sven-Markus Loorits, Jaan Raik
The wide adoption of Vision Transformers (ViTs) in safety-critical applications raises reliability concerns related to hardware faults. Algorithm-Based Fault Tolerance (ABFT) metho…
CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks
Bahram Parchekani, Samira Nazari, Ali Azarpeyvand +3
Deep Neural Networks (DNNs) used in safety-critical applications are vulnerable to hardware and memory faults that corrupt network weights and degrade reliability. In this paper, w…
Effective and Memory-Efficient Alternatives to ECC for Reliable Large-Scale DNNs
Mohammad Hasan Ahmadilivani, Marten Roots, Marco Restifo +3
Modern Deep Learning (DL) workloads are increasingly deployed in safety-critical domains, such as automotive systems and hyperscale data centers, where transient hardware faults po…
Cross-Layer Co-Optimized LSTM Accelerator for Real-Time Gait Analysis
Mohammad Hasan Ahmadilivani, Levent Aksoy, Mohammad Eslami +2
Long Short-Term Memory (LSTM) neural networks have penetrated healthcare applications where real-time requirements and edge computing capabilities are essential. Gait analysis that…
DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining
Muhammad Hassan, Maria Mushtaq, Jaan Raik +1
RISC-V processors are becoming ubiquitous in critical applications, but their susceptibility to microarchitectural side-channel attacks is a serious concern. Detection of microarch…
DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network
Mohammad Hasan Ahmadilivani, Jaan Raik, Masoud Daneshtalab +1
The growing exploitation of Machine Learning (ML) in safety-critical applications necessitates rigorous safety analysis. Hardware reliability assessment is a major concern with res…
ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs
Seyedhamidreza Mousavi, Mohammad Hasan Ahmadilivani, Jaan Raik +2
Deep Neural Networks (DNNs) are extensively employed in safety-critical applications where ensuring hardware reliability is a primary concern. To enhance the reliability of DNNs ag…
Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning
Mohammad Hasan Ahmadilivani, Seyedhamidreza Mousavi, Jaan Raik +2
Convolutional Neural Networks (CNNs) have become integral in safety-critical applications, thus raising concerns about their fault tolerance. Conventional hardware-dependent fault…
SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
Mahdi Taheri, Masoud Daneshtalab, Jaan Raik +5
Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for depl…
AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators
Mahdi Taheri, Natalia Cherezova, Samira Nazari +6
In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators
Mahdi Taheri, Natalia Cherezova, Mohammad Saeed Ansari +4
The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability stand along with the need for reducing the computational burden on the hardware platforms,…
Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Mohammad Hasan Ahmadilivani, Mahdi Taheri, Jaan Raik +2
The superior performance of Deep Neural Networks (DNNs) has led to their application in various aspects of human life. Safety-critical applications are no exception and impose rigo…
Special Session: Approximation and Fault Resiliency of DNN Accelerators
Mohammad Hasan Ahmadilivani, Mario Barbareschi, Salvatore Barone +9
Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this co…
APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors
Mahdi Taheri, Mohammad Hasan Ahmadilivani, Maksim Jenihhin +2
Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical applications raises new reliability concerns. In practice, methods for fault injection by emu…
A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks
Mohammad Hasan Ahmadilivani, Mahdi Taheri, Jaan Raik +2
Artificial Intelligence (AI) and, in particular, Machine Learning (ML) have emerged to be utilized in various applications due to their capability to learn how to solve complex pro…
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
Mahdi Taheri, Mohammad Riazati, Mohammad Hasan Ahmadilivani +5
While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical applications is expanding, emerging DNNs experience massive growth in terms of computation power. I…
DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment
Mohammad Hasan Ahmadilivani, Mahdi Taheri, Jaan Raik +2
Deep Neural Networks (DNNs) and their accelerators are being deployed ever more frequently in safety-critical applications leading to increasing reliability concerns. A traditional…
Hybrid Protection of Digital FIR Filters
Levent Aksoy, Quang-Linh Nguyen, Felipe Almeida +4
A digital Finite Impulse Response (FIR) filter is a ubiquitous block in digital signal processing applications and its behavior is determined by its coefficients. To protect filter…
Survey on Architectural Attacks: A Unified Classification and Attack Model
Tara Ghasempouri, Jaan Raik, Cezar Reinbrecht +2
According to the World Economic Forum, cyber attacks are considered as one of the most important sources of risk to companies and institutions worldwide. Attacks can target the net…
Design Space Exploration of SABER in 65nm ASIC
Malik Imran, Felipe Almeida, Jaan Raik +3
This paper presents a design space exploration for SABER, one of the finalists in NIST's quantum-resistant public-key cryptographic standardization effort. Our design space explora…
High-level Intellectual Property Obfuscation via Decoy Constants
Levent Aksoy, Quang-Linh Nguyen, Felipe Almeida +4
This paper presents a high-level circuit obfuscation technique to prevent the theft of intellectual property (IP) of integrated circuits. In particular, our technique protects a cl…
Side-Channel Attacks on Triple Modular Redundancy Schemes
Felipe Almeida, Levent Aksoy, Jaan Raik +1
The interplay between security and reliability is poorly understood. This paper shows how triple modular redundancy affects a side-channel attack (SCA). Our counterintuitive findin…