11 papers
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…
Unlearning Climate Misinformation in Large Language Models
Michael Fore, Simranjit Singh, Chaehong Lee +3
Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language mod…