The star arboricity of graphs

AbstractA star forest is a forest whose connected components are stars. The star arboricity st(G) of a graph G is the minimum number of star forests whose union covers all edges of G. We show that for every d-regular graph G, 12d<st(G)⩽12d + O(d23(logd)13, and that there are d-regular graphs G with st(G)>12d + omega;(logd). We also observe that the star arboricity of any planar graph is at most 6 and that there are planar graphs whose star arboricity is at least 5

Pessimism Reduction in Crosstalk Noise Aware STA

Abstract — High performance circuits are facing increasingly severe signal integrity problems due to crosstalk noise and crosstalk noise awareness has become an integral part of static timing analysis (STA). Existing crosstalk noise aware STA methods compute noise induced delay uncertainty on a net by net basis and in a pessimistic way, without considering the overlap bounds of the victim and aggressor timing windows and realistic delay impact on early and late signal arrival times. Since crosstalk induced delay on indivudial nets contribute cumulatively on data and clock paths, even small amounts of pessimism in computation can add up to produce several unrealistic timing violations. Unlike glitch noise analysis where noise often attenuates during propagation, quality of delay noise analysis is severely affected by any pessimism in noise estimation and can unnecessarily cost valuable silicon and design resources for fixing unreal violations. In this paper, we propose two temporal techniques to reduce pessimism in crosstalk noise aware STA. The first method, “effective delay noise”, is a net based method where the exact overlap points of victim and aggressor timing windows are considered to obtain the part of delay noise that actually impacts early and late signal arrival times. The second method, “path based delay noise”, is a path based method where the reduced arrival uncertainty of the nets of a given path are utilized for pessimism reduction. We also propose a novel “uncertainty propagation ” technique as part of the second method, which results in an iteration free crosstalk noise aware STA of the path with significantly reduced pessimism. The two techniques are combined in a proposed methodology that is compatible with existing industrial static timing analyzers with very little computational overhead compared to the traditional noise aware STA and a significant improvement in eliminating unreal violations. The proposed techniques resulted in 77% reduction of worst case negative slack and 57 % reduction in the number of failing paths in the setup analysis of a 90nm industrial design. I