activity
20192025
most citedRobust ADAS: Enhancing Robustness of Machine Learning-based Advanced Driver Assistance Systems for Adverse Weather

2 citations · 5 across the 9 of their papers we have counts for

collaborators
Showing cs.ARShow all

7 papers · 1 filter

cs.AR2023

X-Rel: Energy-Efficient and Low-Overhead Approximate Reliability Framework for Error-Tolerant Applications Deployed in Critical Systems

Jafar Vafaei, Omid Akbari, Muhammad Shafique +1

Triple Modular Redundancy (TMR) is one of the most common techniques in fault-tolerant systems, in which the output is determined by a majority voter. However, the design diversity…

cs.AR2023

An Energy-Efficient Generic Accuracy Configurable Multiplier Based on Block-Level Voltage Overscaling

Ali Akbar Bahoo, Omid Akbari, Muhammad Shafique

Voltage Overscaling (VOS) is one of the well-known techniques to increase the energy efficiency of arithmetic units. Also, it can provide significant lifetime improvements, while s…

cs.AR2023

Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications

Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos +4

The challenging deployment of compute-intensive applications from domains such as Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computin…

cs.AR2023

Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos +4

The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications invo…

cs.AR2023

Reduce: A Framework for Reducing the Overheads of Fault-Aware Retraining

Muhammad Abdullah Hanif, Muhammad Shafique

Fault-aware retraining has emerged as a prominent technique for mitigating permanent faults in Deep Neural Network (DNN) hardware accelerators. However, retraining leads to huge ov…

cs.AR2023

FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization

Muhammad Abdullah Hanif, Muhammad Shafique

Permanent faults induced due to imperfections in the manufacturing process of Deep Neural Network (DNN) accelerators are a major concern, as they negatively impact the manufacturin…