Dynamic Partially Reconfiguration (DPR) designs provide additional benefits compared to traditional FPGA application. However, due to the lack of support from automatic design tools in current design flow, designers have to manually define the dimensions and positions of Partially Reconfigurable Regions (PR Regions). The following fine-grained placement for system modules is also limited because it takes the floorplanning result as a rigid region constraint. Therefore, the manual floorplanning is laborious and may lead to inferior fine-grained placement results. In this paper, we propose to integrate PR Region floorplanning with fine-grained placement to achieve the global optimization of the whole DPR system. Effective strategies for tuning PR Region floorplanning and apposite analytical evaluation models are customized for DPR designs to handle the co-optimization for both PR Regions and static region. Not only practical reconfiguration cost and specific reconfiguration constraints for DPR system are considered, but also the congestion estimation can be relaxed by our approach. Especially, we established a two-stage stochastic optimization framework which handles different objectives in different optimization stages so that automated floorplanning and global optimization can be achieved in reasonable time. Experimental results demonstrate that due to the flexibility benefit from the unification of PR Region floorplanning and fine-grained placement, our approach can improve 20.9% on critical path delay, 24% on reconfiguration delay, 12% on congestion, and 8.7% on wire length compared to current DPR design method.
ASTRO-FOLD: A Combinatorial and Global Optimization Framework for Ab Initio Prediction of Three-Dimensional Structures of Proteins from the Amino Acid Sequence
