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.

翻译：本文提出HeLEx框架，用于确定异构空间配置弹性粗粒度可重构阵列（CGRA）的功能布局。给定一组输入数据流图（DFG）及目标CGRA架构，该框架从全功能布局（每个处理单元支持DFG中所有运算操作）出发，采用分支定界搜索算法逐步削减处理单元所支持的运算类型，确保输入DFG能成功映射至生成的CGRA，最终输出优化的异构CGRA。通过对12组DFG和9种目标CGRA规模的实验评估表明：相较于初始全功能布局，该框架平均减少68.7%的运算操作类型，使CGRA面积降低近70%，功耗降低超51%。HeLEx生成的CGRA平均仅比理论最小CGRA（仅支持输入DFG所需精确运算数量的理想架构）大6.2%。与两种前沿框架生成的功能布局对比显示，HeLEx在运算操作削减效果上最高提升2.6倍。