pPIRW: An Efficient and Accurate Precalculation Path Integral Random Walk Solver for Steady-State Thermal Simulation With Robin Boundary Conditions

With the rapid increase of the transistor number in VLSI, rapidly rising power density and temperatures make heat dissipation a major challenge in IC design and manufacturing. However, conventional deterministic thermal analysis methods have difficulties in obtaining local temperature solutions efficiently, and the existing stochastic method is inaccurate when dealing with thermal analysis problems involving Robin boundary conditions (BCs). In this article, a highly parallelized path integral random walk (PIRW) solver is innovatively proposed for steady-state thermal analysis with mixed BCs, especially Robin BCs. The rigorous calculation of the local time and the Feynman-Kac functional $\hat {e}_{c}(t)$ are adopted to accurately handle Neumann and Robin BCs for the first time. Furthermore, based on the PIRW, we propose an accurate and microsecond-level precalculation PIRW (pPIRW) predictor, which precalculates time-consuming random walks, obtains temperatures by simple vector multiplication, and therefore is suitable for proactive thermal management. The pPIRW essentially calculates a partial inverse of large-scale matrices constructed from the finite difference-based compact thermal models (CTMs). Experimental results show that compared with 3D-ICE, the PIRW solver maintains high accuracy with a negligible error within $0.5~^{\circ }$ C, achieves $136\times $ – $209\times $ speedup and $8.53\times $ – $11.1\times $ storage space reduction with all three kinds of BCs, and decreases to $1\times $ – $1.53\times $ speedup for lacking the absorbing Dirichlet boundary. The pPIRW further has speed improvement of 2.5e $4\times $ –6.7e $6\times $ and memory reduction of $36.6\times $ – $42.5\times $ over PIRW without loss of accuracy. Integrated within a thermal management strategy, the pPIRW predictor can eliminate all thermal conflicts while maintaining the highest working frequency. Meanwhile, pPIRW achieves $29.2\times $ speedup and $634\times $ memory reduction over the CTM during the offline precalculation stage.