In this paper, we study the interconnect design problem under a distributed RC delay model. We study the impact of technology factors on the interconnect designs and present general formulations of the interconnect topology design and wiresizing problems. We show that interconnect topology optimization can be achieved by computing optimal generalized rec-

In this paper, we study the optimal wiresizing problem under the distributed Elmore delay model. We show that the optimal wiresizing solutions satisfy a number of interestingproperties, including the separability, the monotone property, and the dominance property. Based on these properties, we develop a polynomial-time optimal wiresizing algorithm for arbitrary interconnect structures under the distributed Elmore delay model. Extensive experimental results show that our wiresizing solution reduces interconnection delay by up to 5i% when compared to the uniform-width solution of the same routing topology. Furthermore, compared to the wiresizing solution based on a simpler RC delay model in [7], our wiresizing solution reduces the total wiring area by up to 28% while further reducing the interconnection delays to the timing-critical sinks by up to 12%.

Given an undirected graph G = V;E with positive e d g e w eights lengths w : E ! + , a set of terminals sinks N V , and a unique root node r 2 N, a shortest-path Steiner arborescence simply called an arborescence in the following is a Steiner tree rooted at r spanning all terminals in N such t h a t e v ery sourceto-sink path is a shortest path in G. G i v en a triple G; N; r, the Minimum Shortest-Path Steiner Arborescence MSPSA problem seeks an arborescence with minimum weight. The MSPSA problem has various applications in the areas of VLSI physical design, multicast network communication, and supercomputer message routing; various cases have been studied in the literature. In this paper, we propose several heuristics and exact algorithms for the MSPSA problem with applications to VLSI physical design. Experiments indicate that our heuristics generate near-optimal results and achieve speedups of orders of magnitude over existing algorithms.

No abstract