Layout optimization is a powerful technique for design migration, circuit performance tuning and design for manufacturing. In this paper, we study the problem of layout optimization for the hierarchical circuits in modern VLSI designs which essentially can be formulated as the Integer Linear Programming(ILP) problem. Existing approaches are either unable to handle hierarchy, inefficient or failing to provide the feasible integer solutions for large scale hierarchical layouts. We present a practical method, IRLS algorithm (Iteratively Rounding and LP Solving) which consists of a proper rounding strategy based on the careful analysis of hierarchical layout constraints, to obtain a feasible integer solution in the constraint-based layout modification process, thus enabling efficient optimization for large scale hierarchical layouts, and specifically avoiding the need to use the general ILP solvers. Experimental results demonstrate the efficiency and effectiveness of the IRLS algorithm. Compared with the general ILP/MILP solver, the IRLS algorithm can obtain decent results with much less runtime (speed-up ranging from 4,000X to 360,000X). Compared with the two-step approach[2] on legalizing a set of large scale industry circuit layouts, the IRLS algorithm can provide much better solution (satisfying all abutment/alignment constraints that the two-step approach fails to meet).

We examine eight different techniques for developing visual rep(cid:173) resentations in machine vision tasks. In particular we compare different versions of principal component and independent com(cid:173) ponent analysis in combination with stepwise regression methods for variable selection. We found that local methods, based on the statistics of image patches, consistently outperformed global meth(cid:173) ods based on the statistics of entire images. This result is consistent with previous work on emotion and facial expression recognition. In addition, the use of a stepwise regression technique for selecting variables and regions of interest substantially boosted performance.

ermine how the reliability of sex discrimination is related to resolution. A normalized pixel-based representation was used for the faces because it explicitly retained texture and shape information while also maintaining geometric relationships. We found that the linear perceptron model can classify sex from facial images with 81% accuracy, compared to 92% accuracy with compression coding on the same data set [6]. The advantage of using a simple linear perceptron with normalized pixel-based inputs is that it allows us to see explicitly those regions of the face that make the largest and most reliable contributions to the classification of sex. A database of 90 faces (44 males, 46 females) was used (O'Toole, Millward, & Anderson [9]). No facial hair, jewelry, or makeup was on any of the faces. Each face was rotated until the eyes were level, and then scaled and cropped so that each image showed a similar facial area. From the original set of faces, we created 5 separate databases at 5