Partial online-synthesis for mixed-grained reconfigurable architectures

  • Authors:
  • Artjom Grudnitsky;Lars Bauer;Jörg Henkel

  • Affiliations:
  • Karlsruhe Institute of Technology (KIT);Karlsruhe Institute of Technology (KIT);Karlsruhe Institute of Technology (KIT)

  • Venue:
  • DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
  • Year:
  • 2012

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Abstract

Processor architectures with Fine-Grained Reconfigurable Accelerators (FGRAs) allow for a high degree of adaptivity to address varying application requirements. When processing computation intensive kernels, multiple FGRAs may be used to execute a complex function. In order to exploit the adaptivity of a fine-grained reconfigurable fabric, a runtime system should decide when and which FGRAs to reconfigure with respect to application requirements. To enable this adaptivity, a flexible infrastructure is required that allows combining FGRAs to execute complex functions. We propose a mixed-grained reconfigurable architecture composed from a Coarse-Grained Reconfigurable Infrastructure (CGRI) that connects the FGRAs. At runtime we synthesize CGRI configurations that depend on decisions of the runtime system, e.g. which FGRAs shall be reconfigured. Synthesis and place & route of the FGRAs are done at compile time for performance reasons. Combined, this results in a partial online synthesis for mixed-grained reconfigurable architectures, which allows maintaining a low runtime overhead while exploiting the inherent adaptivity of the reconfigurable fabric. In this work we focus on the crucial parts of synthesizing the configurations for the CGRI at runtime, propose algorithms, and compare their performance/overhead trade-offs for different application scenarios. We are the first to exploit the increased adaptivity of FGRAs that are connected by a CGRI, by using our partial online synthesis. In comparison to a state-of-the-art reconfigurable architecture that synthesizes the configurations for the CGRI at compile time we obtain an average speedup of 1.79x.