HeLEx：一种异构布局探索器用于空间弹性粗粒度可重构数组
HeLEx: A Heterogeneous Layout Explorer for Spatial Elastic Coarse-Grained Reconfigurable Arrays
AI总结：
HeLEx通过分支定界搜索优化异构CGRAs布局，显著减少操作数量、面积和功率消耗。
摘要：
我们提出了HeLEx，一种用于确定异构空间配置弹性粗粒度可重构数组（CGRAs）功能布局的框架。给定一组输入数据流图（DFGs）和目标CGRAs，该框架首先采用全布局，其中每个处理元素（PE）支持DFGs中的所有操作。然后，它采用分支定界（BB）搜索来消除超出PE的操作，确保输入DFGs能够成功映射到最终的CGRAs，最终返回一个优化的异构CGRAs。使用12个DFGs和9个目标CGRAs大小进行实验评估，结果显示该框架在平均上减少了68.7%的操作数量，从而将CGRAs面积减少近70%，功率减少超过51%，所有数据均与初始全布局相比。HeLEx生成的CGRAs在平均上仅比支持恰好所需操作数量的理论最小CGRAs高出6.2%。与由两个最先进的框架生成的功能布局相比，HeLEx在操作数量的减少方面表现更优，最高可减少2.6倍。
英文摘要：
We present HeLEx, a framework for determining the functional layout of heterogeneous spatially-configured elastic Coarse-Grained Reconfigurable Arrays (CGRAs). Given a collection of input data flow graphs (DFGs) and a target CGRA, the framework starts with a full layout in which every processing element (PE) supports every operation in the DFGs. It then employs a branch-and-bound (BB) search to eliminate operations out of PEs, ensuring that the input DFGs successfully map onto the resulting CGRAs, eventually returning an optimized heterogeneous CGRA. Experimental evaluation with 12 DFGs and 9 target CGRA sizes reveals that the framework reduces the number of operations by 68.7% on average, resulting in a reduction of CGRA area by almost 70% and of power by over 51%, all compared to the initial full layout. HeLEx generates CGRAs that are on average only within 6.2% of theoretically minimum CGRAs that support exactly the number of operations needed by the input DFGs. A comparison with functional layouts produced by two state-of-the-art frameworks indicates that HeLEx achieves better reduction in the number of operations, by up to 2.6X.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
