Reinforcement Learning for Thermal-aware Many-core Task Allocation

Autor:	Wayne Burleson, Russell Tessier, Shiting (Justin) Lu
Rok vydání:	2015
Předmět:	Router Maximum temperature Multi-core processor Many core Thermal aware Computer science Distributed computing Hardware_INTEGRATEDCIRCUITS Reinforcement learning Scheduling (computing)
Zdroj:	ACM Great Lakes Symposium on VLSI
DOI:	10.1145/2742060.2742078
Popis:	To maintain reliable operation, task allocation for many-core processors must consider the heat interaction of processor cores and network-on-chip routers in performing task assignment. Our approach employs reinforcement learning, machine learning algorithm that performs task allocation based on current core and router temperatures and a prediction of which assignment will minimize maximum temperature in the future. The algorithm updates prediction models after each allocation based on feedback regarding the accuracy of previous predictions. Our new algorithm is verified via detailed many-core simulation which includes on-chip routing. Our results show that the proposed technique is fast (scheduling performed in
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::821167c5783ce6126b9bf7e53af412ff https://doi.org/10.1145/2742060.2742078 Zobrazit plný text záznamu