14896427
Multilevel full-chip routing for the X-based architecture
As technology advances into the nanometer territory, the interconnect delay has become a first-order effect on chip performance. To handle this effect, the X-architecture has been proposed for high-performance integrated circuits. The X-architecture presents a new way of orienting a chip's microscopic interconnect wires with the pervasive use of diagonal routes. It can reduce the wire-length and via count, and thus improve performance and routability. Furthermore, the continuous increase of the problem size of IC routing is also a great challenge to existing routing algorithms. In this paper, we present the first multilevel framework for full-chip routing using the X-architecture. To take full advantage of the X-architecture, we explore the optimal routing for three-terminal nets on the X-architecture and develop a general X-Steiner tree algorithm based on the Delaunay triangulation approach for the X-architecture. The multilevel routing framework adopts a two-stage technique of coarsening followed by uncoarsening, with a trapezoid-shaped track assignment embedded between the two stages to assign long, straight diagonal segments for wirelength reduction. Compared with the state-of-the-art multilevel routing for the Manhattan architecture, experimental results show that our approach reduced wirelength by 18.7% and average delay by 8.8% with similar routing completion rates and via counts.
14756669
Multilevel routing with antenna avoidance
As technology advances into the nanometer territory, the antenna problem has caused significant impact on routing tools. The antenna effect is a phenomenon of plasma-induced gate oxide degradation caused by charge accumulation on conductors. It directly influences manufacturability and yield of VLSI circuits, especially in deep-submicron technology using high density plasma. Furthermore, the continuous increase of the problem size of IC routing is also a great challenge to existing routing algorithms. In this paper, we propose a novel framework for multilevel full-chip routing with antenna avoidance using a built-in jumper insertion approach. Experimental results show that our approach reduced antenna-violated gates by about 98% and also achieved 100% routing completion for all circuits.
1324170
A Random Linear Network Coding Approach to Multicast
We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks
211733
Using Case-Based Reasoning in Interpreting Unsupervised Inductive Learning Results
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The interpretive process is considered as an intrinsic part in OSHAM and is carried out by a combination of case-based reasoning with matching approaches in inductive learning. An experimental comparative study of some learning methods in terms of knowledge description and prediction is given.
52850401
Optimization Based Rate Control for Multicast with Network Coding
Recent advances in network coding have shown great potential for efficient information multicasting in communication networks, in terms of both network throughput and network management. In this paper, we address the problem of rate control at end-systems for network coding based multicast flows. We develop two adaptive rate control algorithms for the networks with given coding subgraphs and without given coding subgraphs, respectively. With random network coding, both algorithms can be implemented in a distributed manner, and work at transport layer to adjust source rates and at network layer to carry out network coding. We prove that the proposed algorithms converge to the globally optimal solutions for intra-session network coding. Some related issues are discussed, and numerical examples are provided to complement our theoretical analysis.
27312959
Sorting a sequence of strong kings in a tournament
A king in a tournament is a player who beats any other player directly or indirectly. According to the existence of a king in every tournament, Wu and Sheng [Inform. Process. Lett. 79 (2001) 297-299] recently presented an algorithm for finding a sorted sequence of kings in a tournament of size n, i.e., a sequence of players u1, u2,....,un such that ui → ui+1 (ui beats ui+1) and ui is a king in the sub-tournament induced by {ui, ui+1,.....,un} for each i = 1, 2,....,n - 1. With each pair u,v of players in a tournament, let b(u,v) denote the number of third players used for u to beat v indirectly. Then, a king u is called a strong king if the following condition is fulfilled: if v → u then b(u, v) > b(v,u). In the sequel, we will show that the algorithm proposed by Wu and Sheng indeed generates a sorted sequence of strong kings, which is more restricted than the previous one.
3251340
A Knowledge Discovery System with Support for Model Selection and Visualization
The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.