DTOC: integrating Deep-learning driven Timing Optimization into the state-of-the-art Commercial EDA tool

Recently, deep-learning (DL) models have paid a considerable attention to timing prediction in the placement and routing (P&R) flow. As yet, the DL-based prior works are confined to timing prediction at the time-consuming global routing stage, and very few have addressed the timing prediction problem at the placement, i.e., at the pre-route stage. This is because it is not easy to “accurately” predict various timing parameters at the pre-route stage. Moreover, no work has addressed a seamless link of timing prediction at the pre-route stage to the final timing optimization through making use of commercial P&R tools. In this work, we propose a framework called DTOC, to be used at the pre-route stage for this end. Precisely, the framework is composed of two models: (1) a DL-driven arc delay and arc output slew prediction model, performing in two levels: (level-1) predicting net resistance (R), net capacitance (C), and arc length (Len), followed by (level-2) predicting arc delay and arc output slew from the R/C/Len prediction obtained in (level-1); (2) a timing optimization model, which uses the inference outcomes in our DL-driven prediction model to enable the commercial P&R tools to calculate the full path delays, setting update timing margins on paths, so that the P&R tools should use more accurate margins on timing optimization. Experimental results show that, by using our DTOC framework during timing optimization in P&R, we improve the pre-route prediction accuracy on arc delay and arc output slew by 20~26% on average, and improve the WNS, TNS, and the number of timing violation paths by 50~63 % on average.