Gate sizing is a fundamental netlist optimization move and researchers have used supervised learning-based models in gate sizers. Recently, Reinforcement Learning (RL) has been tried for sizing gates (and other EDA optimization problems) but are very runtime-intensive. In this work, we explore a novel Transformer-based gate sizer, TransSizer, to directly generate optimized gate sizes given a placed and unoptimized netlist. TransSizer is trained on datasets obtained from real tapeout-quality industrial designs in a foundry 5nm technology node. Our results indicate that TransSizer achieves 97% accuracy in predicting optimized gate sizes at the postroute optimization stage. Furthermore, TransSizer has a speedup of ~1400× while delivering similar timing, power and area metrics when compared to a leading-edge commercial tool for sizing-only optimization.
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