Paper accepted: Towards an Energy-Efficient Video Processing Tool with LLMs

Authors: Zoha Azimi (Alpen-Adria Universität Klagenfurt, Austria), Reza Farahani (Alpen-Adria Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria Universität Klagenfurt, Austria), Radu Prodan (Alpen-Adria Universität Klagenfurt, Austria)

Event: ACM 4th Mile-High Video Conference (MHV’25), 18–20 February 2025 | Denver, CO, USA

Abstract: Large language models (LLMs), the backbone of generative artificial intelligence (AI) like ChatGPT, have become more widely integrated in different fields, including multimedia. The rising number of conversational queries on such platforms now emits as much CO2 as everyday activities, leading to an exponential growth of energy consumption and underscoring urgent sustainability challenges. This short paper introduces an energy-aware LLM-based video processing tool. Employing open-source LLM models and techniques like fine-tuning and Retrieval-Augmented Generation (RAG), this tool recommends video processing commands and executes them in an energy-aware manner. Preliminary results show that it achieves reduced energy consumption per prompt compared to baselines.