Dependency-Aware Parallel Routing for Large-Scale FPGAs

Quantitative effects of Moore's Law have driven qualitative changes in FPGA architecture, applications, and tools. As a consequence, the existing EDA tools takes several hours or even days to implement the applications onto FPGAs. Typically, routing is a very time-consuming process in the EDA design flow. While several attempts have accelerated this process through parallelization, they still do not provide a strong parallel scheme for FPGA routing. In this paper we introduce a dependency-aware parallel approach, named Bamboo, to accelerate the routing time for FPGAs. With the dependency detection, Bamboo partitions the nets into multiple subsets, where the nets in the same subsets are independent, and the dependency only exists among different subsets. Specifically, the independent nets in the same subset are routed in parallel, and the subsets are processed in serial according to the original routing ordering. The partitioning problem is solved optimally using dynamic programming, and the parallelization is implemented by speculative parallelism on a single GPU. Experimental results show that our approach achieves an average of 15.13x speedup with negligible influence on the routing quality. Most importantly, it effectively maintains deterministic results and always produces the same results as the serial version.