34 citations · 113 across the 17 of their papers we have counts for
8 papers · 1 filter
Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning
Syed Mhamudul Hasan, Hussein Zangoti, Iraklis Anagnostopoulos +1
Recent studies have shown that sponge attacks can significantly increase the energy consumption and inference latency of deep neural networks (DNNs). However, prior work has focuse…
Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers
Varatheepan Paramanayakam, Andreas Karatzas, Dimitrios Stamoulis +1
Edge computing systems struggle to efficiently manage multiple concurrent deep neural network (DNN) workloads while meeting strict latency requirements, minimizing power consumptio…
Multi-Agent Geospatial Copilots for Remote Sensing Workflows
Chaehong Lee, Varatheepan Paramanayakam, Andreas Karatzas +7
We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows. Unlike existing single-agent approaches that rely on…
RankMap: Priority-Aware Multi-DNN Manager for Heterogeneous Embedded Devices
Andreas Karatzas, Dimitrios Stamoulis, Iraklis Anagnostopoulos
Modern edge data centers simultaneously handle multiple Deep Neural Networks (DNNs), leading to significant challenges in workload management. Thus, current management systems must…
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization
Konstantinos Balaskas, Andreas Karatzas, Christos Sad +4
Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential…
OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload
Andreas Karatzas, Iraklis Anagnostopoulos
Modern Deep Neural Networks (DNNs) exhibit profound efficiency and accuracy properties. This has introduced application workloads that comprise of multiple DNN applications, raisin…
Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Ourania Spantidi, Georgios Zervakis, Iraklis Anagnostopoulos +1
Deep Neural Networks (DNNs) are being heavily utilized in modern applications and are putting energy-constraint devices to the test. To bypass high energy consumption issues, appro…
Control Variate Approximation for DNN Accelerators
Georgios Zervakis, Ourania Spantidi, Iraklis Anagnostopoulos +2
In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Mont…