Performance-driven Global Placement via Adaptive Network Characterization

Delay minimization continues to be an important objective in the design of high-performance computing system. In this paper, we present an effective methodology to guide the delay optimization process of the mincut-based global placement via adaptive sequential network characterization. The contribution of this work is the development of a fully automated approach to determine critical parameters related to performance-driven multi-level partitioning-based global placement with retiming. We validate our approach by incorporating this adaptive method into a state-of-the-art global placer GEO. Our A-GEO, the adaptive version of GEO, achieves 67% maximum and 22% average delay improvement over GEO