Modulo scheduling with integrated register spilling for clustered VLIW architectures

Clustering is a technique to decentralize the design of future wide issue VLIW cores and enable them to meet the technology constraints in terms of cycle time, area and power dissipation. In a clustered design, registers and functional units are grouped in clusters so that new instructions are neede...

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Vydané v:	Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture s. 160 - 169
Hlavní autori:	Zalamea, Javier, Llosa, Josep, Ayguadé, Eduard, Valero, Mateo
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	Washington, DC, USA IEEE Computer Society 01.01.2001
Edícia:	ACM Conferences
Predmet:	Software and its engineering Software and its engineering > Software notations and tools Software and its engineering > Software notations and tools > Compilers Theory of computation Theory of computation > Design and analysis of algorithms Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms Theory of computation > Design and analysis of algorithms > Online algorithms Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms > Scheduling algorithms Theory of computation > Models of computation Theory of computation > Models of computation > Concurrency Theory of computation > Models of computation > Concurrency > Parallel computing models Theory of computation > Theory and algorithms for application domains Theory of computation > Theory and algorithms for application domains > Machine learning theory Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making
ISBN:	0769513697, 9780769513690
ISSN:	1072-4451
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