Issue No.05 - September/October (2009 vol.29)
José F. Martínez , Cornell University
Engin İpek , University of Rochester
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MM.2009.77
<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.
José F. Martínez, Engin İpek, "Dynamic Multicore Resource Management: A Machine Learning Approach", IEEE Micro, vol.29, no. 5, pp. 8-17, September/October 2009, doi:10.1109/MM.2009.77