34 citations · 35 across the 8 of their papers we have counts for
8 papers
A Vertical Approach to Designing and Managing Sustainable Heterogeneous Edge Data Centers
Aikaterini Maria Panteleaki, Varatheepan Paramanayakam, Vasileios Pentsos +3
The increasing demand for Artificial Intelligence (AI) computing poses significant environmental challenges, with both operational and embodied carbon emissions becoming major cont…
CarbonCall: Sustainability-Aware Function Calling for Large Language Models on Edge Devices
Varatheepan Paramanayakam, Andreas Karatzas, Iraklis Anagnostopoulos +1
Large Language Models (LLMs) enable real-time function calling in edge AI systems but introduce significant computational overhead, leading to high power consumption and carbon emi…
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…
LLM-dCache: Improving Tool-Augmented LLMs with GPT-Driven Localized Data Caching
Simranjit Singh, Michael Fore, Andreas Karatzas +6
As Large Language Models (LLMs) broaden their capabilities to manage thousands of API calls, they are confronted with complex data operations across vast datasets with significant…
An LLM-Tool Compiler for Fused Parallel Function Calling
Simranjit Singh, Andreas Karatzas, Michael Fore +2
State-of-the-art sequential reasoning in Large Language Models (LLMs) has expanded the capabilities of Copilots beyond conversational tasks to complex function calling, managing th…
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…