Accurate Wirelength Prediction for Placement-Aware Synthesis through Machine Learning
Daijoon Hyun, Yuepeng Fan and Youngsoo Shin
School of Electrical Engineering, KAIST, Daejeon, Korea
ABSTRACT
Placement-aware synthesis, which combines logic synthesis with virtual placement and routing (P&R) to better take account of wiring, has been popular for timing closure. The wirelength after virtual placement is correlated to actual wirelength, but correlation is not strong enough for some chosen paths. An algorithm to predict the actual wirelength from placement-aware synthesis is presented. It extracts a number of parameters from a given virtual path. A handful of synthetic parameters are compiled through linear discriminant analysis (LDA), and they are submitted to a few machine learning models. The final prediction of actual wirelength is given by the weighted sum of prediction from such machine learning models, in which weight is determined by the population of neighbors in parameter space. Experiments indicate that the predicted wirelength is 93% accurate compared to actual wirelength; this can be compared to conventional virtual placement, in which wirelength is predicted with only 79% accuracy.
