Title :
Dynamic Multicore Resource Management: A Machine Learning Approach
Author :
Martínez, José F. ; Ipek, Engin
Author_Institution :
Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
Abstract :
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. Machine learning is the study of computer programs and algorithms that learn about their environment and improve automatically with experience.This approach thus contrasts with today´s predominant approach of directly specifying at design time how the hardware should accomplish the desired goal. This results in more efficient and flexible management of critical hardware resources at runtime.
Keywords :
learning (artificial intelligence); microprocessor chips; multiprocessing systems; planning (artificial intelligence); computer algorithm; computer program; critical hardware resource; dynamic multicore resource management; flexible management; machine learning approach; planning approach; self-optimizing on-chip hardware agent; Algorithm design and analysis; Hardware; Machine learning; Machine learning algorithms; Multicore processing; Resource management; Runtime; dynamic resource management; machine learning.; multicore;
Journal_Title :
Micro, IEEE