Online scheduling for multi-core shared reconfigurable fabric

Processor customization in the form of application-specific instructions has become a popular choice to meet the increasing performance demands of embedded applications under short time-to-market constraints. Implementing the custom instructions in reconfigurable logic provides greater flexibility....

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Vydáno v:Proceedings of the Conference on Design, Automation and Test in Europe s. 582 - 585
Hlavní autoři: Chen, Liang, Marconi, Thomas, Mitra, Tulika
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: San Jose, CA, USA EDA Consortium 12.03.2012
Edice:ACM Conferences
Témata:
Computer systems organization > Architectures > Parallel architectures
Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling 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 > Interactive computation
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Online learning theory
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making
ISBN:3981080181, 9783981080186
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Abstract Processor customization in the form of application-specific instructions has become a popular choice to meet the increasing performance demands of embedded applications under short time-to-market constraints. Implementing the custom instructions in reconfigurable logic provides greater flexibility. Recently, a number of architectures have been proposed where multiple cores on chip share a single reconfigurable fabric that implements the custom instructions. Effective exploitation of this reconfigurable fabric requires runtime scheduling of the tasks on the cores and allocation of reconfigurable logic for custom instructions. In this paper, we propose an efficient online scheduling algorithm for multi-core shared reconfigurable fabric and show its effectiveness through experimental evaluation.
AbstractList Processor customization in the form of application-specific instructions has become a popular choice to meet the increasing performance demands of embedded applications under short time-to-market constraints. Implementing the custom instructions in reconfigurable logic provides greater flexibility. Recently, a number of architectures have been proposed where multiple cores on chip share a single reconfigurable fabric that implements the custom instructions. Effective exploitation of this reconfigurable fabric requires runtime scheduling of the tasks on the cores and allocation of reconfigurable logic for custom instructions. In this paper, we propose an efficient online scheduling algorithm for multi-core shared reconfigurable fabric and show its effectiveness through experimental evaluation.
Author Marconi, Thomas
Chen, Liang
Mitra, Tulika
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  givenname: Tulika
  surname: Mitra
  fullname: Mitra, Tulika
  email: tulika@comp.nus.edu.sg
  organization: National University of Singapore
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SubjectTerms Computer systems organization -- Architectures -- Parallel architectures
Theory of computation -- Design and analysis of algorithms -- Approximation algorithms analysis -- Scheduling 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 -- Interactive computation
Theory of computation -- Theory and algorithms for application domains -- Machine learning theory -- Online learning theory
Theory of computation -- Theory and algorithms for application domains -- Machine learning theory -- Reinforcement learning -- Sequential decision making
Title Online scheduling for multi-core shared reconfigurable fabric
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