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Auflistung nach Autor:in "Begic Fazlic, Lejla"

1 - 3 von 3
  • Konferenzbeitrag
    LLMs on the Edge: Quality, Latency, and Energy Efficiency
    (INFORMATIK 2024, 2024) Bast, Sebastian; Begic Fazlic, Lejla; Naumann, Stefan; Dartmann, Guido
    Generative Artificial Intelligence has become integral to many people's lives, with Large Language Models (LLMs) gaining popularity in both science and society. While training these models is known to require significant energy, inference also contributes substantially to their total energy consumption. This study investigates how to use LLMs sustainably by examining the efficiency of inference, particularly on local hardware with limited computing resources. We develop metrics to quantify the efficiency of LLMs on edge devices, focusing on quality, latency, and energy consumption. Our comparison of three state-of-the-art generative models on edge devices shows that they achieve quality scores ranging from 73.3% to 85.9%, generate 1.83 to 3.51 tokens per second, and consume between 0.93 and 1.76 mWh of energy per token on a single-board computer without GPU support. The findings suggest that generative models can produce satisfactory outcomes on edge devices, but thorough efficiency evaluations are recommended before deployment in production environments.
  • Konferenzbeitrag
    Sustainability in Artificial Intelligence - Towards a Green AI Reference Model
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Weber, Sebastian; Guldner, Achim; Begic Fazlic, Lejla; Dartmann, Guido; Naumann, Stefan
    The interest in Green Artificial Intelligence (AI) is growing as AI research is increasingly focusing on and taking into account environmental sustainability. This paper aims to clarify and emphasize the distinction between terms like sustainable AI, Green AI, Green by AI, and Green in AI, highlighting their importance in the context of environmentally responsible AI practices. We find that existing Green Software reference models are insufficient for meeting the unique requirements of Green AI. Thus, we argue that a tailored Green AI reference model is needed to guide and promote environmentally responsible practices in the field of AI, addressing the special considerations associated with Green AI.
  • Konferenzbeitrag
    Towards Sustainable Machine Learning: Analyzing Energy-Efficient Algorithmic Strategies for Environmental Sensor Data
    (INFORMATIK 2024, 2024) Cetkin, Berkay; Begic Fazlic, Lejla; Guldner, Achim; Naumann, Stefan; Dartmann, Guido
    This study evaluates the energy efficiency of machine learning (ML) classification models across 49 test setups, each representing different conditions derived from a set of scenarios. Utilizing internet of things (IoT) technology with an ESP8266 microcontroller, we collected and analyzed environmental data including temperature, humidity, and CO2 levels from a simulated room environment. We measured energy consumption for data preprocessing, model training, and testing, alongside energy efficiency metrics that consider output, processing time, and F1 score. The study also performed correlation analyses to explore the relationship between energy consumption and performance metrics. Furthermore, it assessed the trade-offs between accuracy and energy efficiency by comparing an ensemble model to its constituent algorithms. The measurements, conducted according to the Green Software Measurement Model (GSMM), provide essential insights into selecting energy-efficient algorithms for a broad spectrum of IoT applications.