AI-Driven CPU Performance Profiling: Optimizing Power Efficiency and Computational Workloads
Year: 2025
Type: article
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.
Institution: The University of Texas at Dallas
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409772003