Delay driven multi-way circuit partitioning.

Wong Sze Hon.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 88-91).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Preliminaries --- p.1Chapter 1.2 --- Motivations --- p.1Chapter 1.3 --- Contributions --- p.3Chapter 1.4 --- Organization of the Thesis --- p.4Chapter 2 --- VLSI Physical Design Automation --- p.5Chapter 2.1 --- Preliminaries --- p.5Chapter 2.2 --- VLSI Design Cycle [1] --- p.6Chapter 2.2.1 --- System Specification --- p.6Chapter 2.2.2 --- Architectural Design --- p.6Chapter 2.2.3 --- Functional Design --- p.6Chapter 2.2.4 --- Logic Design --- p.8Chapter 2.2.5 --- Circuit Design --- p.8Chapter 2.2.6 --- Physical Design --- p.8Chapter 2.2.7 --- Fabrication --- p.8Chapter 2.2.8 --- Packaging and Testing --- p.9Chapter 2.3 --- Physical Design Cycle [1] --- p.9Chapter 2.3.1 --- Partitioning --- p.9Chapter 2.3.2 --- Floorplanning and Placement --- p.11Chapter 2.3.3 --- Routing --- p.11Chapter 2.3.4 --- Compaction --- p.12Chapter 2.3.5 --- Extraction and Verification --- p.12Chapter 2.4 --- Chapter Summary --- p.12Chapter 3 --- Recent Approaches on Circuit Partitioning --- p.14Chapter 3.1 --- Preliminaries --- p.14Chapter 3.2 --- Circuit Representation --- p.15Chapter 3.3 --- Delay Modelling --- p.16Chapter 3.4 --- Partitioning Objectives --- p.19Chapter 3.4.1 --- Interconnections between Partitions --- p.19Chapter 3.4.2 --- Delay Minimization --- p.19Chapter 3.4.3 --- Area and Number of Partitions --- p.20Chapter 3.5 --- Partitioning Algorithms --- p.20Chapter 3.5.1 --- Cut-size Driven Partitioning Algorithm --- p.21Chapter 3.5.2 --- Delay Driven Partitioning Algorithm --- p.32Chapter 3.5.3 --- Acyclic Circuit Partitioning Algorithm --- p.33Chapter 4 --- Clustering Based Acyclic Multi-way Partitioning --- p.38Chapter 4.1 --- Preliminaries --- p.38Chapter 4.2 --- Previous Works on Clustering Based Partitioning --- p.39Chapter 4.2.1 --- Multilevel Circuit Partitioning [2] --- p.40Chapter 4.2.2 --- Cluster-Oriented Iterative-Improvement Partitioner [3] --- p.42Chapter 4.2.3 --- Section Summary --- p.44Chapter 4.3 --- Problem Formulation --- p.45Chapter 4.4 --- Clustering Based Acyclic Multi-Way Partitioning --- p.46Chapter 4.5 --- Modified Fan-out Free Cone Decomposition --- p.47Chapter 4.6 --- Clustering Phase --- p.48Chapter 4.7 --- Partitioning Phase --- p.51Chapter 4.8 --- The Acyclic Constraint --- p.52Chapter 4.9 --- Experimental Results --- p.57Chapter 4.10 --- Chapter Summary --- p.58Chapter 5 --- Network Flow Based Multi-way Partitioning --- p.61Chapter 5.1 --- Preliminaries --- p.61Chapter 5.2 --- Notations and Definitions --- p.62Chapter 5.3 --- Net Modelling --- p.63Chapter 5.4 --- Previous Works on Network Flow Based Partitioning --- p.64Chapter 5.4.1 --- Network Flow Based Min-Cut Balanced Partitioning [4] --- p.65Chapter 5.4.2 --- Network Flow Based Circuit Partitioning for Time-multiplexed FPGAs [5] --- p.66Chapter 5.5 --- Proposed Net Modelling --- p.70Chapter 5.6 --- Partitioning Properties Based on the Proposed Net Modelling --- p.73Chapter 5.7 --- Partitioning Step --- p.75Chapter 5.8 --- Constrained FM Post Processing Step --- p.79Chapter 5.9 --- Experiment Results --- p.81Chapter 6 --- Conclusion --- p.86Bibliography --- p.8

Reformulated acyclic partitioning for rail-rail containers transshipment

Many rail terminals have loading areas that are properly equipped to move containers between trains. With the growing throughput of these terminals all the trains involved in a sequence of such movements may not ¿t in the loading area simultaneously, and storage areas are needed to place containers waiting for their destination train, although this storage increases the cost of the transshipment. This increases the complexity of the planning decisions concerning these activities, since now trains need to be packed in groups that ¿t in the loading area, in such a way that the number of containers moved to the storage area is minimized. Additionally, each train is only allowed to enter the loading area once. Similarly to previous authors, we model this situation as an acyclic graph partitioning problem for which we present a new formulation, and several valid inequalities based on its theoretical properties. Our computational experiments show that the new formulation outperforms the previously existing ones, providing results that improve even on the best exact algorithm designed so far for this problem.Peer ReviewedPostprint (author's final draft

Clustering Based Acyclic Multi-way Partitioning

In this paper, we present a clustering based algorithm for acyclic multi-way partitioning. Many existing partitioning algorithms have shown that clustering can effectively improve the solution quality. However, most of them do not consider the signal direction and thus cannot maintain the acyclic property. Our algorithm is based on clustering by computing the modified fan-out free cones. Fan-out free cone clustering can reduce a graph to a smaller and sparser one, and maintain the acyclic property at the same time. Experimental results showed that our algorithm compares favorably with the previous best acyclic multi-way partitioning algorithm in cut-size