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| 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. | |||
| BibTex | x | ||
| @article{ 10.1109/MM.2009.77, author = {José F. Martínez and Engin İpek}, title = {Dynamic Multicore Resource Management: A Machine Learning Approach}, journal ={IEEE Micro}, volume = {29}, number = {5}, issn = {0272-1732}, year = {2009}, pages = {8-17}, doi = {http://doi.ieeecomputersociety.org/10.1109/MM.2009.77}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - IEEE Micro TI - Dynamic Multicore Resource Management: A Machine Learning Approach IS - 5 SN - 0272-1732 SP8 EP17 EPD - 8-17 A1 - José F. Martínez, A1 - Engin İpek, PY - 2009 KW - multicore KW - dynamic resource management KW - machine learning. VL - 29 JA - IEEE Micro ER - | |||
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.
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