62726183
Further improve circuit partitioning using GBAW logic perturbation techniques
Efficient circuit partitioning is gaining more importance with the increasing size of modern circuits. Conventionally, circuit partitioning is solved by modeling a circuit as a hypergraph for the ease of applying graph algorithms. However there exists room for further improvement on even optimum hypergraph partitioning results, if logic information can be applied for perturbation. In this paper we present a multi-way partitioning framework which can couple any excellent hypergraph partitioner and a noval logic perturbation based technique (GBAW) for further improvement over very excellent partitioning results. Our approach can integrate with any graph partitioner. We performed experiments on 2-, 3-, 4-, and 5-way partitionings for various circuits of different sizes from MCNC benchmarks. We have chosen the state-of-the-art hMetis-Kway to obtain high quality initial solutions for the experiments. Our experiments showed that this partitioning approach can achieve a further 15% reduction in cut size for 2-way partitioning with an area penalty of only 0.33%. The good results demonstrated the effectiveness of this new partitioning technique.
594928
Graph based analysis of 2-D FPGA routing
In this paper, we study the two-dimensional FPGA, Xilinx-like routing architectures and present the first known computational complexity results for them. The routing problem is formulated as a two-dimensional interval packing problem and is proved to be NP-complete with or without doglegs. Next, we consider other routing structures obtained from the industrial one by arbitrarily changing switch box connection topology while maintaining the same connection flexibility. There is an exponentially large number of such routing structures. We further prove that there does not exist a better routing architecture among the members of this large domain. In addition, we prove that there is no constant bound on the mapping ratio of a track number required by a detailed routing to a global routing channel density for the studied architectures. Finally, we show two directions of changing the routing architectures which yield polynomial time mapping solutions and constant bounded mapping ratios. Our theoretical analysis is intended to give some insight to, and understanding of this new routing problem's fundamental properties.
16244697
OBDD Minimization Based on Two-Level Representation of Boolean Functions
In this paper, we analyze the basic properties of some Boolean function classes and propose a low complexity OBDD variable ordering algorithm, which is exact (optimum) to some classes of functions and very effective to general two-level form functions. We show that the class of series-parallel functions, which can be expressed by a factored form where each variable appears exactly once, can yield exact OBDD variable orderings in polynomial time. We also study the thin Boolean functions whose corresponding OBDDs can be represented by the form of thin OBDDs in which the number of nonterminal nodes is equal to the number of input variables. We show,that a thin Boolean function always has an essential prime cube cover and the class of series-parallel functions is a proper subset of thin Boolean functions. We propose a heuristic viewing OBDDs as evaluation machines with function cube covers as their inputs and apply a queuing principle in the algorithm design. Our heuristic, the augmented Dynamic Shortest Cube First algorithm, is proven to be optimum for the series-parallel functions and also be very effective for general two-level form functions. Experimental results on a large number of two-level form benchmark circuits show that the algorithm yields an OBDD total size reduction of over 51 percent with only 7 percent CPU time compared to the well-known network-based Fan-in Heuristic implemented in the SIS package. Comparing to the known exact results, ours is only 49 percent larger in size while only uses 0.001 percent CPU time.
1673387
Two-Stage Optimal Component Analysis
Linear representations are widely used to reduce dimension in applications involving high dimensional data. While specialized procedures exist for certain optimality criteria, such as principle component analysis (PCA) and Fisher discriminant analysis (FDA), they can not be generalized for more general criteria. To overcome this fundamental limitation, optimal component analysis (OCA) uses a stochastic gradient optimization procedure intrinsic to the manifold giving by the constraints of applications and therefore gives a procedure for finding optimal representations for general criteria. However, due to its generality nature, OCA often requires extensive computation for gradient estimation and updating. To significantly reduce the required computation, in this paper, we propose a two-stage method by first reducing the dimension of input to a smaller one (but larger than the final resulting dimension) using a computationally efficient method and then performing OCA in the reduced space. This reduces the computation time from days to minutes on widely used databases, making OCA learning feasible for many applications. Additionally, since the reduced space is much smaller, the stochastic gradient optimization tends to be more efficient. We illustrate the effectiveness of the proposed method on face classification.
5950644
Hiding Sensitive Association Rules with Limited Side Effects
Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., nonsensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process. In this paper, we present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. Since the correlation among rules can make it impossible to achieve this goal, in this paper, we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified entries. The experimental results show the effectiveness of our approach, i.e., undesired side effects are avoided in the rule hiding process. The results also report that in most cases, all the sensitive rules are hidden without spurious rules falsely generated. Moreover, the good scalability of our approach in terms of database size and the influence of the correlation among rules on rule hiding are observed
1702698
Rendering of 3D-wavelet-compressed concentric mosaic scenery with progressive inverse wavelet synthesis (PIWS)
The concentric mosaics offer a quick solution to the construction and navigation of a virtual environment. To reduce the vast data amount of the concentric mosaics, a compression scheme based on 3D wavelet transform has been proposed in a previous paper. In this work, we investigate the efficient implementation of the renderer. It is preferable not to expand the compressed bitstream as a whole, so that the memory consumption of the renderer can be reduced. Instead, only the data necessary to render the current view are accessed and decoded. The progressive inverse wavelet synthesis (PIWS) algorithm is proposed to provide the random data access and to reduce the calculation for the data access requests to a minimum. A mixed cache is used in PIWS, where the entropy decoded wavelet coefficient, intermediate result of lifting and fully synthesized pixel are all stored at the same memory unit because of the in- place calculation property of the lifting implementation. PIWS operates with a finite state machine, where each memory unit is attached with a state to indicate what type of content is currently stored. The computational saving achieved by PIWS is demonstrated with extensive experiment results.
13420664
Music Classification Using Significant Repeating Patterns
With the popularity of multimedia applications, a large amount of music data has been accumulated on the Internet. Automatic classification of music data becomes a critical technique for providing an efficient and effective retrieval of music data. In this paper, we propose a new approach for classifying music data based on their contents. In this approach, we focus on monophonic music features represented as rhythmic and melodic sequences. Moreover, we use repeating patterns of music data to do music classification. For each pattern discovered from a group of music data, we employ a series of measurements to estimate its usefulness for classifying this group of music data. According to the patterns contained in a music piece, we determine which class it should be assigned to. We perform a series of experiments and the results show that our approach performs on average better than the approach based on the probability distribution of contextual information in music.
8596758
TIMBER: a native system for querying XML
XML has become ubiquitous, and XML data has to be managed in databases. The current industry standard is to map XML data into relational tables and store this information in a relational database. Such mappings create both expressive power problems and performance problems.In the TIMBER [7] project we are exploring the issues involved in storing XML in native format. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.