RePlAce: Advancing Solution Quality and Routability Validation in Global Placement

The Nesterov’s method approach to analytic placement [27] [28] [29] has recently demonstrated strong solution quality and scalability. We dissect the previous implementation strategy of [29] and show that solution quality can be significantly improved using two levers: constraint-oriented local smoothing, and dynamic step size adaptation. We propose a new density function that comprehends local overflow of area resources; this enables a constraint-oriented local smoothing at per-bin granularity. Our improved dynamic step size adaptation automatically determines step size and effectively allocates optimization effort to significantly improve solution quality without undue runtime impact. Our resulting global placement tool, RePlAce, achieves an average of 2.00% HPWL reduction over all best known ISPD-2005 and ISPD-2006 benchmark results, and an average of 2.73% over all best known MMS benchmark results, without any benchmark-specific code or tuning. We further extend our global placer to address routability, and achieve on average 8.50% to 9.59% scaled HPWL reduction over previous leading academic placers for the DAC-2012 and ICCAD-2012 benchmark suites. To our knowledge, RePlAce is the first work to achieve superior solution quality across all the ISPD-2005, ISPD-2006, MMS, DAC-2012 and ICCAD-2012 benchmark suites with a single global placement engine.