The problem of creating efficient mappings of dataflow graphs onto specific architectures (i.e., solving the place and route problem) is incredibly challenging. The difficulty is especially acute in the area of Coarse-Grained Reconfigurable Architectures (CGRAs) to the extent that solving the mapping problem may remove a significant bottleneck to adoption. We believe that the next generation of mapping algorithms will exhibit pattern recognition, the ability to learn from experience, and identification of creative solutions, all of which are human characteristics. This manuscript describes our game UNTANGLED, developed and fine-tuned over the course of a year to allow us to capture and analyze human mapping strategies. It also describes our results to date. We find that the mapping problem can be crowdsourced very effectively, that players can outperform existing algorithms, and that successful player strategies share many elements in common. Based on our observations and analysis, we make concrete recommendations for future research directions for mapping onto CGRAs.

Figure 1: The student-created pipeline facilitates a 20-80-100% completion paradigm. First, assets were taken from creation to render at a 20% completion level (first image), to work out the pipeline. Next, technical issues were addressed while taking shots to 80% completion (second image). The final image is 100% complete, showing the result of numerous artistic iterations.