9 citations · 9 across the 5 of their papers we have counts for
4 papers · 1 filter
Certified Robustness via Sparse Second-Order Cone Relaxations
Mohammadreza Maleki, Daniel Sadig, Rushendra Sidibomma +3
Certified robustness is critical for deploying neural networks in safety-sensitive settings, but computing exact certificates typically requires solving nonconvex optimization prob…
MEDEM: Multi-Engine DL Accelerator Design Methodology
Fareed Qararyah, Mohammad Ali Maleki, Pedro Trancoso
Multi-engine deep learning (DL) accelerators are becoming increasingly prevalent as they address the heterogeneity and growing complexity of modern DL workloads. To efficiently pro…
CEAR: Certified Ensemble Adversarial Robustness in DNNs
Daniel Sadig, Mohammadreza Maleki, Hamed Karimi +1
Deep Neural Networks (DNNs) are highly susceptible to adversarial perturbations, leading to extensive research on robustness for safety-critical applications. State-of-the-art empi…
SISA: A Scale-In Systolic Array for GEMM Acceleration
Luigi Altamura, Alessio Cicero, Mateo Vázquez Maceiras +2
The currently dominant AI/ML workloads, such as Large Language Models (LLMs), rely on the efficient execution of General Matrix-Matrix Multiplication (GEMM) operations. Thus, most…