Late Breaking Results: BLAST: Bisection-Free Learning Approach for Statistical Timing Characterization

Statistical timing characterization for standard cells faces significant computational challenges due to the laborious bisection analysis for setup/hold constraint of sequential cells. To address this issue, we propose a Bisection-Free Learning Approach for Statistical Timing Characterization (BLAST) by extracting inherent delay for data path and clock path in sequential cells as specific features. Multi-task learning is implemented with a multi-gate mixture-of-experts (MMoE) model to exploit the profound interdependency between setup and hold constraint for different timing arcs, where the active learning strategy is incorporated to improve learning efficiency. Experimental results under 135 PVT corners with TSMC 12nm process demonstrate that the proposed BLAST achieves considerable acceleration by avoiding the iterative bisection search for statistical constraint prediction with 76.9% runtime reduction compared to the commercial tool. Excellent prediction accuracy is achieved for various flip-flops by BLAST with the relative root mean square error (rRMSE) of $\mathbf{2. 2 1 \%}$ and worst-case absolute error (WCAE) of 0.82 ps.