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Computer Power Consumption while using Ad-Blocker on a System with AI Accelerators

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  • Khan Awais Khan
  • Mohammad Tariq Iqbal
  • Mohsin Iqbal

Abstract

This study investigates the impact of ad-blockers on system power consumption in a computing environment equipped with an AI accelerator. The increasing prevalence of online advertisements has raised concerns about system performance and energy efficiency, prompting many users to turn to ad-blockers. However, the effectiveness of ad-blockers on power consumption, especially in systems equipped with specialized AI accelerators, remains underexplored. In this research, we evaluate the power usage, GPU utilization, and memory consumption of computers running ad-blockers on both Windows and Ubuntu operating systems. The study compared traditional CPU/GPU methods with AI-accelerated scenarios, using popular ad-blockers such as AdBlock, Adblock Plus, uBlock, uBlock Origin, and uBlock Origin Lite. Results indicate that uBlock Origin and uBlock Origin Lite were the most efficient, significantly reducing power consumption and memory usage compared to other ad-blockers. However, multimedia-heavy websites presented challenges, with increased resource usage observed. The findings emphasize the importance of choosing appropriate ad-blockers to enhance energy efficiency, optimize system resources, and contribute to sustainable computing.

Suggested Citation

  • Khan Awais Khan & Mohammad Tariq Iqbal & Mohsin Iqbal, 2025. "Computer Power Consumption while using Ad-Blocker on a System with AI Accelerators," European Journal of Information Technologies and Computer Science, European Open Science, vol. 5(1), pages 11-20, January.
    • Handle: RePEc:epw:comput:v:5:y:2025:i:1:id:10144
    DOI: 10.24018/compute.2025.5.1.144
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    References listed on IDEAS

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    1. Azalia Mirhoseini & Anna Goldie & Mustafa Yazgan & Joe Wenjie Jiang & Ebrahim Songhori & Shen Wang & Young-Joon Lee & Eric Johnson & Omkar Pathak & Azade Nova & Jiwoo Pak & Andy Tong & Kavya Srinivasa, 2021. "A graph placement methodology for fast chip design," Nature, Nature, vol. 594(7862), pages 207-212, June.
    2. Maurizio Capra & Beatrice Bussolino & Alberto Marchisio & Muhammad Shafique & Guido Masera & Maurizio Martina, 2020. "An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks," Future Internet, MDPI, vol. 12(7), pages 1-22, July.
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