Data-structure builder for VLSI/CAD software

Relational database systems have successfully solved many business data processing problems. The primary reason of this success is that the relational data model provides a simple, yet flexible view of data as tables. In studying VLSI/CAD data, we noticed that they are often represented in formats similar to relational tuples. Therefore, they can be stored easily in relational tables. However, it is generally agreed that conventional relational database systems are inefficient for VLSI/CAD applications, since such applications often access large amounts of data repetitively. In order to solve this problem, we designed and implemented a data mapping subsystem that converts VLSI/CAD data stored in relational tables into internal data structures so that they can be efficiently manipulated in C. By using our data mapping language, we could reduce the amount of code required by the data-structure construction parts of some real VLSI/ CAD tools to about 1/10 of that required by C implementation. Our data-structure builder consumes several times more CPU cycles

URK: Utah robot kit - A 3-link robot manipulator prototype

Journal ArticleIn designing robot manipulators, the interaction between several modules (S/W, VLSI, CAD, CAM, Robotics, and Control) illustrates an interdisciplinary prototyping environment that includes different types of information that are radically different but combined in a coordinated way. This paper describes the analysis and design of a 3-link robot manipulator prototype as part of a research project for building a prototyping environment for electro-mechanical systems. This prototype robot will be used as an educational tool in robotics and control classes

Reconfigurable Tree Architectures Using Subtree Oriented Fault Tolerance

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryMicroelectronics and Computer Technology Corporation (MCC) / VLSI/CAD grantNational Aeronautics and Space Administration / NASA NAG 1-613AT&T Bell Laboratories fellowshipOpe

Similarity-Aware Spectral Sparsification by Edge Filtering

In recent years, spectral graph sparsification techniques that can compute ultra-sparse graph proxies have been extensively studied for accelerating various numerical and graph-related applications. Prior nearly-linear-time spectral sparsification methods first extract low-stretch spanning tree from the original graph to form the backbone of the sparsifier, and then recover small portions of spectrally-critical off-tree edges to the spanning tree to significantly improve the approximation quality. However, it is not clear how many off-tree edges should be recovered for achieving a desired spectral similarity level within the sparsifier. Motivated by recent graph signal processing techniques, this paper proposes a similarity-aware spectral graph sparsification framework that leverages efficient spectral off-tree edge embedding and filtering schemes to construct spectral sparsifiers with guaranteed spectral similarity (relative condition number) level. An iterative graph densification scheme is introduced to facilitate efficient and effective filtering of off-tree edges for highly ill-conditioned problems. The proposed method has been validated using various kinds of graphs obtained from public domain sparse matrix collections relevant to VLSI CAD, finite element analysis, as well as social and data networks frequently studied in many machine learning and data mining applications

Crosstalk minimization of local channel routing algorithms in VLSI CAD

The greedy and left edge algorithms, as applied to local routing in VLSI CAD, were modified to decrease crosstalk between neighboring wires. The modifications on the algorithms use spacing, and segregation to improve the routing of wires in a channel. The modified greedy and left edge algorithms use a grid, but the minimum distance between two wires can be varied depending on the crosstalk between them. Crosstalk information must be obtained separately and is part of the required set of inputs to the algorithms. The improved algorithms route all the channel problems tested in less tracks than the original algorithms, if crosstalk constraints exist, and in the same number of tracks, if no crosstalk constraints exist