Feature-Aware Task-to-Core Allocation in Embedded Multi-Core Platforms via Statistical Learning
Year: 2025
Type: conference-paper
Abstract: Optimizing task-to-core allocation can substantially reduce power consumption in multi-core platforms without degrading user experience. However, existing approaches overlook critical factors such as parallelism, compute intensity, and heterogeneous core types. In this paper, we introduce a statistical learning approach for feature selection that identifies the most influential features-such as core type, speed, temperature, and application-level parallelism or memory intensity-for accurate environment modeling and efficient energy minimization, a critical consideration for embedded systems. Our experiments, conducted with state-of-the-art Linux governors and thermal modeling techniques, show that correlation-aware task-to-core allocation lowers energy consumption by up to 10 % and reduces core temperature by up to$5^{\circ} \mathrm{C}$compared to random core selection. Furthermore, our compressed, bootstrapped regression model improves thermal prediction accuracy by 6 % while cutting model parameters by 16 %, yielding an overall mean square error reduction of 61.6 % relative to existing approaches. We provided results based on superscalar Intel Core i7 12th Gen processors with 14 cores, and validated our method across a diverse set of hardware platforms and effectively balanced performance, power, and thermal demands through statistical feature evaluation.
Authors: Mohammad Pivezhandi, Abusayeed Saifullah, Prashant Modekurthy
Institutions: Wayne State University, University of Nevada, Las Vegas
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CNS Core: Small: Low-Power Wide-Area Networks for Industrial Automation, G2184075278, CAREER: Protocols for Low-Power Wide-Area Networks in White Spaces, Collaborative Research: CNS Core: Medium: Parallel and Real-Time Multicore Scheduling for an Efficiently-Used Cache (PARSEC), G6671297155, G8876996369
OpenAlex ID: W7084110810