Ink: Efficient Incremental k-Critical Path Generation
Che Chang, Tsung-Wei Huang, Dian-Lun Lin, Guannan Guo, Shiju Lin
摘要
Critical Path Generation (CPG) is crucial for static timing analysis (STA) applications to validate timing constraints. Recent years have witnessed CPG algorithms that can rank k critical paths efficiently and accurately. However, they all suffer from the lack of incrementality, which is the ability to quickly update critical paths after the circuit is incrementally modified. To solve this problem, we introduce Ink, an efficient incremental CPG algorithm. Inspired by the large path trace similarity between adjacent CPG queries, Ink identifies a set of paths to reuse for the next query and effectively prunes the path search space. We have demonstrated the promising performance of Ink on large circuit benchmarks. Ink is up to 22.4X faster and consumes up to 31% less memory than a state-of-the-art timer when generating one million paths on a large design.
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