Fast Chip Transient Temperature Simulation via Machine Learning

With growing transistor densities, analyzing temperature in 2D and 3D integrated circuits (ICs) is becoming more complicated and critical. Finite-element solvers give accurate results, but a single transient run can take hours or even days. Compact thermal models (CTMs) shorten the temperature simulation running time using a numerical solver based on the duality between thermal and electric properties. However, CTM solvers often still take hours for small-scale chips because of iterative numerical solvers. Recent work using machine learning (ML) models creates a fast and reliable framework for predicting temperature. However, current ML models demand large input samples and hours of GPU training to reach acceptable accuracy.To overcome the challenges stated, we design an ML framework that couples with CTMs to accelerate steady-state and transient thermal analysis without large data inputs. Our framework combines principal-component analysis (PCA) with closed-form linear regression to predict the on-chip temperature directly. The linear regression weights are solved analytically, so training for a grid size of 512×512 finishes in under a minute with only 15–20 CTM samples. Experimental results show that our framework can achieve more than 33× and 49.6× speedup for steady-state and transient simulation of a chip with a 245.95mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> footprint, keeping the mean squared error below 0.1°C<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.