2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024
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…
cs.DC2023★ 2 cited
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…