Presentation
PPA-driven Placement via Adaptive Cluster Constraints Optimization
DescriptionThe clustering-based placement framework has demonstrated promising potential in improving the efficiency and quality of very-large-scale integration (VLSI) placement.
However, existing methods typically impose unified and rule-based constraints on different clusters, overlooking the unique intra- and inter-cluster connection properties that vary across clusters, which leads to suboptimal results.To address this challenge and promote effective PPA optimization, we introduce an innovative PPA-driven placement paradigm with mixed-grained Adaptive Cluster Constraints Optimization (ACCO), which applies constraints with tailored constraint tightness to different clusters, balancing local and global interactions for improved placement performance. Specifically, we propose a novel eBound model with quantified constraint tightness, combined with a Bayesian optimizer to dynamically adjust the constraints for each cluster based on PPA outcomes, which are ultimately passed on to the final flat placement. Experimental results on benchmarks across various domains show that our methods can achieve up to 62%, 97% and 25% improvements in post-route WNS, TNS and power compared to existing methods.
However, existing methods typically impose unified and rule-based constraints on different clusters, overlooking the unique intra- and inter-cluster connection properties that vary across clusters, which leads to suboptimal results.To address this challenge and promote effective PPA optimization, we introduce an innovative PPA-driven placement paradigm with mixed-grained Adaptive Cluster Constraints Optimization (ACCO), which applies constraints with tailored constraint tightness to different clusters, balancing local and global interactions for improved placement performance. Specifically, we propose a novel eBound model with quantified constraint tightness, combined with a Bayesian optimizer to dynamically adjust the constraints for each cluster based on PPA outcomes, which are ultimately passed on to the final flat placement. Experimental results on benchmarks across various domains show that our methods can achieve up to 62%, 97% and 25% improvements in post-route WNS, TNS and power compared to existing methods.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby