We propose, implement, and evaluate a novel approach for mapping dataflow graphs (DFGs) onto spatially configured Coarse-Grained Reconfigurable Arrays (CGRAs). The approach tackles mapping failure due to the congestion that arises when more than one routing path uses the same CGRA link. Heuristics are used to identify congestion patterns and "reserve" CGRA processing elements (PEs) around the congestion by preventing them from being used for DFG nodes in a mapping re-attempt. The reserved PEs effectively increases routing resources around the congestion, thereby increasing the likelihood of mapping success. This approach is referred to as reserve-on-demand mapping since PEs are reserved only when congestion exists and is driven by its patterns.
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Chen et al. (2024) studied this question.
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