17 citations · 27 across the 7 of their papers we have counts for
7 papers
4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič +52
The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy an…
MC3: Memory Contention based Covert Channel Communication on Shared DRAM System-on-Chips
Ismet Dagli, James Crea, Soner Seckiner +3
Shared-memory system-on-chips (SM-SoC) are ubiquitously employed by a wide-range of mobile computing platforms, including edge/IoT devices, autonomous systems and smartphones. In S…
Context-aware Multi-Model Object Detection for Diversely Heterogeneous Compute Systems
Justin Davis, Mehmet E. Belviranli
In recent years, deep neural networks (DNNs) have gained widespread adoption for continuous mobile object detection (OD) tasks, particularly in autonomous systems. However, a preva…
H-EYE: Holistic Resource Modeling and Management for Diversely Scaled Edge-Cloud Systems
Ismet Dagli, Amid Morshedlou, Jamal Rostami +1
Computing systems have been evolving to be more pervasive, heterogeneous, and dynamic. An increasing number of emerging domains now rely on diverse edge to cloud continuum where th…
Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips
Ismet Dagli, Mehmet Belviranli
Two distinguishing features of state-of-the-art mobile and autonomous systems are 1) there are often multiple workloads, mainly deep neural network (DNN) inference, running concurr…
Optimizing Regular Expressions via Rewrite-Guided Synthesis
Jedidiah McClurg, Miles Claver, Jackson Garner +3
Regular expressions are pervasive in modern systems. Many real-world regular expressions are inefficient, sometimes to the extent that they are vulnerable to complexity-based attac…
A Computational-Graph Partitioning Method for Training Memory-Constrained DNNs
Fareed Qararyah, Mohamed Wahib, Doğa Dikbayır +2
Many state-of-the-art Deep Neural Networks (DNNs) have substantial memory requirements. Limited device memory becomes a bottleneck when training those models. We propose ParDNN, an…