Quality Estimation of Approximate Computing Using Machine Learning (MS Thesis) (Record no. 68371)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02444nam a22001337a 4500 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Anjum Usman |
| -- | 21-MCSE-05 |
| -- | Supervisor - Prof.Dr. Ubedullah Rajput |
| 245 ## - TITLE STATEMENT | |
| Title | Quality Estimation of Approximate Computing Using Machine Learning (MS Thesis) |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Nawabshah |
| Name of publisher | QUEST |
| Year of publication | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 90p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | ABSTRACT<br/><br/>This thesis focuses on approximate computing, a strategy to balance performance and energy efficiency by trading a slight loss of accuracy for significant speed and power gains. However, ensuring acceptable result quality remains a challenge. The study introduces a novel approach that utilizes machine learning to predict the quality of approximate computations. The goal is to create a robust framework that helps designers make informed decisions between accuracy and efficiency. A comprehensive dataset is compiled, containing diverse approximate computing instances with quality metrics. Various features from the dataset, including computation traits and error rates, are used as inputs for machine learning models. After rigorous exploration of machine learning algorithms, a refined model that demonstrates strong predictive performance across various inaccurate computing scenarios is selected.<br/><br/>In order to enhance the capacity to forecast outcomes in approximate computing more precisely, this research applies transfer learning techniques. Pretrained models are more accurate than the originally trained model when they are adjusted using specific, imperfect computational input. The framework consistently produces correct quality estimates for a variety of computations and has undergone extensive validation through a battery of tests. It aids in the decision-making process for highly effective and energy-efficient system designers. The framework's flexibility and generalizability are validated by empirical evaluation of real-world applications. In order to assess approximation accuracy, a unique machine-learning process is proposed in this study. It illustrates how effective transfer learning is in this situation. This approach helps designers to effectively control the trade-off between accuracy and efficiency in the rapidly developing field of approximation computing, which is becoming more and more crucial as the need for efficient computing systems grows.<br/><br/>xii |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Department of Computer Systems Engineering |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://tinyurl.com/5n8bfkbs |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Thesis and Dissertation |
| Withdrawn status | Lost status | Home library | Current library | Date acquired | Accession Number | Koha item type |
|---|---|---|---|---|---|---|
| Research Section | Research Section | 27/05/2024 | MP/89-1286 | Thesis and Dissertation | ||
| Research Section | Research Section | 27/05/2024 | MP/89-1287 | Thesis and Dissertation | ||
| Research Section | Research Section | 27/05/2024 | MP/89-1288 | Thesis and Dissertation | ||
| Research Section | Research Section | 26/07/2024 | MP/91-1319 | Thesis and Dissertation |