Neural Self-Organization Based 3D Rectilinear Steiner Minimal Tree Generation


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Agrawal, R and Mehrotra, R and Mandal, AS (2012) Neural Self-Organization Based 3D Rectilinear Steiner Minimal Tree Generation. In: IEEE UK Sim 14th International Conference on Modelling and Simulation (IEEE UKSim 2012), March 28 - 30, 2012, Cambridge University, UK. (Submitted)

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Given N points in a plane, generation of Rectilinear Steiner Minimal Tree (RSMT) is always a challenging problem (NP hard) with numerous applications. As the number of points increases, the complexity of the problem increases exponentially. A neural self organization based method with linear complexity and linear memory requirements has been used for generation of Rectilinear Steiner Minimal Tree in 3D space. The system is initialized by constructing an open curve around the given set of points and each given point is connected to the nearest point generated on the open curve. An energy equation is framed reflecting the length of the system and the total energy of the system is subsequently minimized iteratively using Neural Networks. The nature of the open curve and its other parameters are determined experimentally. The methodology will have significant applications in multilayer VLSI/ULSI interconnection design and for resource connections in any plant design.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: RSTM; self-organizing; neural network; NP hard ;circuit design
Subjects: ?? TK ??
Semiconductor Devices > IC Design
Divisions: Semiconductor Devices
Depositing User: Mr. Rohit Singh
Date Deposited: 22 May 2013 10:04
Last Modified: 22 May 2013 10:04

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