International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 2
March-April 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-Driven CPU Performance Profiling: Optimizing Power Efficiency and Computational Workloads

Author(s) Mr. Manoj Chowdary Lingam, Mr. Ram Bhaskar Rakesh Marada
Country United States
Abstract High performance computing in variety of industries has made the requirement of increasing CPU performance at the lowest power consumption more critical. The traditional profiling techniques, which are known to some extent, suffer in dynamically responding to the real time variations in the workload and the energy efficiency requirements. This paper discusses a paradigm shift in methods of improving power efficiency and managing computational workload using the AI-Driven CPU Performance Profiling. Machine learning algorithms and predictive analytics are integrated in AI based profiling and can be used to autonomously monitor, evaluate and optimize the processor performance parameters in real time. Various AI methodologies that are used for the detection of performance bottlenecks, prediction of thermal thresholds and balance of core utilization for varying into the study such as supervised learning, reinforcement learning, and deep neural networks. In addition, it studies how to extract lesson from previous performance data using AI models to produce actionable insights for task scheduling, thread allocation, and dynamic voltage and frequency scaling (DVFS) to decrease energy consumption overall while retaining the processing rate.
In order to validate the effectiveness of AI driven profiling for various operational contexts, performance gains and energy savings are assessed in terms of key metrics (instructions per cycle (IPC), cache hit rates and power delay product). In addition to being an advancement in the field of intelligent hardware optimization, this research indicates that AI driven profiling not only leads to more efficient computation, but also integrates well with other sustainability goals of computing infrastructure. Finally, the paper recommends an integration of the AI profiling into next generation CPU architectures and development ecosystems.
Keywords CPU profiling, artificial intelligence, power efficiency, workload optimization, performance analytics
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-24
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.41478
Short DOI https://doi.org/g9gh8f
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com