An Investigation of Clock Skew Using a Wirelength-Aware Floorplanning Process in the Pre-Placement Stages of MSV Layouts

Managing the timing constraints has become an important factor in the physical design of multiple supply voltage (MSV) integrated circuits (IC). Clock distribution and module scheduling are some of the conventional methods used to satisfy the timing constraints of a chip. In this paper, we propose a simulated annealing-based MSV floorplanning methodology for the design of ICs within the timing budget. Additionally, we propose a modified SKB tree representation for floorplanning the modules in the design. Our algorithm finds the optimal dimensions and position of the clocked modules in the design to reduce the wirelength and satisfy the timing constraints. The proposed algorithm is implemented in IWLS 2005 benchmark circuits and considers power, wirelength, and timing as the optimization parameters. Simulation results were obtained from the Cadence Innovus digital system taped-out at 45 nm. Our simulation results show that the proposed algorithm satisfies timing constraints through a 30.6% reduction in wirelength.

Unanimous and Strategy-Proof Probabilistic Rules for Single-Peaked Preference Profiles on Graphs

Finitely many agents have preferences on a finite set of alternatives, single-peaked with respect to a connected graph with these alternatives as vertices. A probabilistic rule assigns to each preference profile a probability distribution over the alternatives. First, all unanimous and strategy-proof probabilistic rules are characterized when the graph is a tree. These rules are uniquely determined by their outcomes at those preference profiles at which all peaks are on leaves of the tree and, thus, extend the known case of a line graph. Second, it is shown that every unanimous and strategy-proof probabilistic rule is random dictatorial if and only if the graph has no leaves. Finally, the two results are combined to obtain a general characterization for every connected graph by using its block tree representation.

Non-Overlapping LZ77 Factorization and LZ78 Substring Compression Queries with Suffix Trees

We present algorithms computing the non-overlapping Lempel–Ziv-77 factorization and the longest previous non-overlapping factor table within small space in linear or near-linear time with the help of modern suffix tree representations fitting into limited space. With similar techniques, we show how to answer substring compression queries for the Lempel–Ziv-78 factorization with a possible logarithmic multiplicative slowdown depending on the used suffix tree representation.

REPRESENTATION BY STRUCTURE OF SPANNING TREE

The most effective representation of the class of ordinary graphs (in the sense of in-formation capacity) is representation of the structure of the trees. The paper considers the different ways of the graphs assignments. The theorem on the possibility of networks representation by a tree structure is given. In proving this theorem the necessary and sufficient conditions compliance with their representation is formulated. The issues of representations transformation are considered. An example of network coding is given. The network can be set by any tree representation if this represen-tation sets a tree accurate within the numbering of all its vertices.

Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances

The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, e.g., corners, line-segments and shapes, in an image and extracting features from those keypoints. In this paper, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by utilising a multi-tree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six hand-crafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.