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A Polynomial Placement Algorithm for Data Driven Coarse-Grained Reconfigurable Architectures

机译:数据驱动的粗粒度可重构体系结构的多项式放置算法

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Coarse-grained reconfigurable computing architectures vary widely in the number and characteristics of the processing elements (cells) and routing topologies used. In order to exploit several different topologies, a place and route framework, able to deal with such vast design exploration space, is of paramount importance. Bearing this in mind, this paper proposes a placement scheme able to target different topologies when considering data-driven reconfigurable architectures. Our approach uses graph models for the target architecture and for the dataflow representation of the application being mapped. Our placement algorithm is guided by a depth-first traversal in both the architecture and the application graphs. Two versions of the placement algorithm with respectively O(e) and O(e + n3) computational complexities are presented, where e is the number of edges in the dataflow representation of the application and n is the number of cells in the graph model of the architecture. The achieved experimental results show that our approach can be useful to exploit different interconnect topologies as far as coarse-grained reconfigurable computing architectures are concerned
机译:粗粒度可重配置计算体系结构在所使用的处理元素(单元)和路由拓扑的数量和特性方面差异很大。为了开发几种不同的拓扑结构,能够应对如此巨大的设计探索空间的布局布线框架至关重要。考虑到这一点,本文提出了一种在考虑数据驱动的可重配置架构时能够针对不同拓扑的布局方案。我们的方法将图形模型用于目标体系结构和要映射的应用程序的数据流表示。我们的布局算法在体系结构图和应用图中均以深度优先遍历为指导。提出了两种版本的放置算法,分别具有O(e)和O(e + n 3 )计算复杂度,其中e是应用程序的数据流表示中的边数,n是架构图模型中的单元数量。取得的实验结果表明,就粗粒度可重构计算架构而言,我们的方法可用于开发不同的互连拓扑

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