Interconnect-driven floorplanning.

Sham Chiu Wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 107-113).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivations --- p.1Chapter 1.2 --- Progress on the Problem --- p.2Chapter 1.3 --- Our Contributions --- p.3Chapter 1.4 --- Thesis Organization --- p.5Chapter 2 --- Preliminaries --- p.6Chapter 2.1 --- Introduction --- p.6Chapter 2.1.1 --- The Role of Floorplanning --- p.6Chapter 2.1.2 --- Wirelength Estimation --- p.7Chapter 2.1.3 --- Different Types of Floorplan --- p.8Chapter 2.2 --- Representations of Floorplan --- p.10Chapter 2.2.1 --- Polish Expressions --- p.10Chapter 2.2.2 --- Sequence Pair --- p.11Chapter 2.2.3 --- Bounded-Sliceline Grid (BSG) Structure --- p.13Chapter 2.2.4 --- O-Tree --- p.14Chapter 2.2.5 --- B*-Tree --- p.16Chapter 2.2.6 --- Corner Block List --- p.18Chapter 2.2.7 --- Twin Binary Tree --- p.19Chapter 2.2.8 --- Comparisons between Different Representations --- p.20Chapter 2.3 --- Algorithms of Floorplan Design --- p.20Chapter 2.3.1 --- Constraint Based Floorplanning --- p.21Chapter 2.3.2 --- Integer Programming Based Floorplanning --- p.21Chapter 2.3.3 --- Neural Learning Based Floorplanning --- p.22Chapter 2.3.4 --- Rectangular Dualization --- p.22Chapter 2.3.5 --- Simulated Annealing --- p.23Chapter 2.3.6 --- Genetic Algorithm --- p.23Chapter 2.4 --- Summary --- p.24Chapter 3 --- Literature Review on Interconnect-Driven Floorplanning --- p.25Chapter 3.1 --- Introduction --- p.25Chapter 3.2 --- Simulated Annealing Approach --- p.25Chapter 3.2.1 --- """Pepper - A Timing Driven Early Floorplanner""" --- p.25Chapter 3.2.2 --- """A Timing Driven Block Placer Based on Sequence Pair Model""" --- p.26Chapter 3.2.3 --- """Integrated Floorplanning and Interconnect Planning""" --- p.27Chapter 3.2.4 --- """Interconnect Driven Floorplanning with Fast Global Wiring Planning and Optimization""" --- p.27Chapter 3.3 --- Genetic Algorithm Approach --- p.28Chapter 3.3.1 --- "“Timing Influenced General-cell Genetic Floorplanning""" --- p.28Chapter 3.4 --- Force Directed Approach --- p.29Chapter 3.4.1 --- """Timing Influenced Force Directed Floorplanning""" --- p.29Chapter 3.5 --- Congestion Planning --- p.30Chapter 3.5.1 --- """On the Behavior of Congestion Minimization During Placement""" --- p.30Chapter 3.5.2 --- """Congestion Minimization During Placement""" --- p.31Chapter 3.5.3 --- "“Estimating Routing Congestion Using Probabilistic Anal- ysis""" --- p.31Chapter 3.6 --- Buffer Planning --- p.32Chapter 3.6.1 --- """Buffer Block Planning for Interconnect Driven Floor- planning""" --- p.32Chapter 3.6.2 --- """Routability Driven Repeater Block Planning for Interconnect- centric Floorplanning""" --- p.33Chapter 3.6.3 --- """Provably Good Global Buffering Using an Available Block Plan""" --- p.34Chapter 3.6.4 --- "“Planning Buffer Locations by Network Flows""" --- p.34Chapter 3.6.5 --- """A Practical Methodology for Early Buffer and Wire Re- source Allocation""" --- p.35Chapter 3.7 --- Summary --- p.36Chapter 4 --- Floorplanner with Fixed Buffer Planning [34] --- p.37Chapter 4.1 --- Introduction --- p.37Chapter 4.2 --- Overview of the Floorplanner --- p.38Chapter 4.3 --- Congestion Model --- p.38Chapter 4.3.1 --- Construction of Grid Structure --- p.39Chapter 4.3.2 --- Counting the Number of Routes at a Grid --- p.40Chapter 4.3.3 --- Buffer Location Computation --- p.41Chapter 4.3.4 --- Counting Routes with Blocked Grids --- p.42Chapter 4.3.5 --- Computing the Probability of Net Crossing --- p.43Chapter 4.4 --- Time Complexity --- p.44Chapter 4.5 --- Simulated Annealing --- p.45Chapter 4.6 --- Wirelength Estimation --- p.46Chapter 4.6.1 --- Center-to-center Estimation --- p.47Chapter 4.6.2 --- Corner-to-corner Estimation --- p.47Chapter 4.6.3 --- Intersection-to-intersection Estimation --- p.48Chapter 4.7 --- Multi-pin Nets Handling --- p.49Chapter 4.8 --- Experimental Results --- p.50Chapter 4.9 --- Summary --- p.51Chapter 5 --- Floorplanner with Flexible Buffer Planning [35] --- p.53Chapter 5.1 --- Introduction --- p.53Chapter 5.2 --- Overview of the Floorplanner --- p.54Chapter 5.3 --- Congestion Model --- p.55Chapter 5.3.1 --- Probabilistic Model with Variable Interval Buffer Inser- tion Constraint --- p.57Chapter 5.3.2 --- Time Complexity --- p.61Chapter 5.4 --- Buffer Planning --- p.62Chapter 5.4.1 --- Estimation of Buffer Usage --- p.62Chapter 5.4.2 --- Estimation of Buffer Resources --- p.69Chapter 5.5 --- Two-phases Simulated Annealing --- p.70Chapter 5.6 --- Wirelength Estimation --- p.72Chapter 5.7 --- Multi-pin Nets Handling --- p.73Chapter 5.8 --- Experimental Results --- p.73Chapter 5.9 --- Remarks --- p.76Chapter 5.10 --- Summary --- p.76Chapter 6 --- Global Router --- p.77Chapter 6.1 --- Introduction --- p.77Chapter 6.2 --- Overview of the Global Router --- p.77Chapter 6.3 --- Buffer Insertion Constraint and Congestion Constraint --- p.78Chapter 6.4 --- Multi-pin Nets Handling --- p.79Chapter 6.5 --- Routing Methodology --- p.79Chapter 6.6 --- Implementation --- p.80Chapter 6.7 --- Summary --- p.86Chapter 7 --- Interconnect-Driven Floorplanning by Alternative Packings --- p.87Chapter 7.1 --- Introduction --- p.87Chapter 7.2 --- Overview of the Method --- p.87Chapter 7.3 --- Searching Alternative Packings --- p.89Chapter 7.3.1 --- Rectangular Supermodules in Sequence Pair --- p.89Chapter 7.3.2 --- Finding rearrangable module sets --- p.90Chapter 7.3.3 --- Alternative Sequence Pairs --- p.94Chapter 7.4 --- Implementation --- p.97Chapter 7.4.1 --- Re-calculation of Interconnect Cost --- p.98Chapter 7.4.2 --- Cost Function --- p.101Chapter 7.4.3 --- Time Complexity --- p.101Chapter 7.5 --- Experimental Results --- p.101Chapter 7.6 --- Summary --- p.103Chapter 8 --- Conclusion --- p.105Bibliography --- p.10

Timing-Driven Macro Placement

Placement is an important step in the process of finding physical layouts for electronic computer chips. The basic task during placement is to arrange the building blocks of the chip, the circuits, disjointly within a given chip area. Furthermore, such positions should result in short circuit interconnections which can be routed easily and which ensure all signals arrive in time. This dissertation mostly focuses on macros, the largest circuits on a chip. In order to optimize timing characteristics during macro placement, we propose a new optimistic timing model based on geometric distance constraints. This model can be computed and evaluated efficiently in order to predict timing traits accurately in practice. Packing rectangles disjointly remains strongly NP-hard under slack maximization in our timing model. Despite of this we develop an exact, linear time algorithm for special cases. The proposed timing model is incorporated into BonnMacro, the macro placement component of the BonnTools physical design optimization suite developed at the Research Institute for Discrete Mathematics. Using efficient formulations as mixed-integer programs we can legalize macros locally while optimizing timing. This results in the first timing-aware macro placement tool. In addition, we provide multiple enhancements for the partitioning-based standard circuit placement algorithm BonnPlace. We find a model of partitioning as minimum-cost flow problem that is provably as small as possible using which we can avoid running time intensive instances. Moreover we propose the new global placement flow Self-Stabilizing BonnPlace. This approach combines BonnPlace with a force-directed placement framework. It provides the flexibility to optimize the two involved objectives, routability and timing, directly during placement. The performance of our placement tools is confirmed on a large variety of academic benchmarks as well as real-world designs provided by our industrial partner IBM. We reduce running time of partitioning significantly and demonstrate that Self-Stabilizing BonnPlace finds easily routable placements for challenging designs – even when simultaneously optimizing timing objectives. BonnMacro and Self-Stabilizing BonnPlace can be combined to the first timing-driven mixed-size placement flow. This combination often finds placements with competitive timing traits and even outperforms solutions that have been determined manually by experienced designers