Computer Power Consumption while using Ad-Blocker on a System with AI Accelerators
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".