Issue No. 05 - September/October (2009 vol. 29)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MM.2009.77
José F. Martínez , Cornell University
Engin İpek , University of Rochester
<p>A machine learning approach to multicore resource management produces self-optimizing on-chip hardware agents capable of learning, planning, and continuously adapting to changing workload demands. This results in more efficient and flexible management of critical hardware resources at runtime.</p>
multicore, dynamic resource management, machine learning.
E. İpek and J. F. Martínez, "Dynamic Multicore Resource Management: A Machine Learning Approach," in IEEE Micro, vol. 29, no. , pp. 8-17, 2009.