2 citations · 5 across the 4 of their papers we have counts for
4 papers
Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers
Mohammad Javad Askarizadeh, Ebrahim Farahmand, Jorge Castro-Godinez +3
Deep Neural Networks (DNNs) have advanced in many real-world applications, such as healthcare and autonomous driving. However, their high computational complexity and vulnerability…
An Edge-based WiFi Fingerprinting Indoor Localization Using Convolutional Neural Network and Convolutional Auto-Encoder
Amin Kargar-Barzi, Ebrahim Farahmand, Nooshin Taheri Chatrudi +2
With the ongoing development of Indoor Location-Based Services, the location information of users in indoor environments has been a challenging issue in recent years. Due to the wi…
scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation
Ebrahim Farahmand, Mohammad Javad Askarizadeh, Ali Mahani +4
In this paper, we propose a scalable approximate multiplier design, scaleTRIM, that approximates the multiplication operation using fitted linear functions, also referred to as lin…
Design and Analysis of High Performance Heterogeneous Block-based Approximate Adders
Ebrahim Farahmand, Ali Mahani, Muhammad Abdullah Hanif +1
Approximate computing is an emerging paradigm to improve the power and performance efficiency of error-resilient applications. As adders are one of the key components in almost all…