5735225
Buffer insertion with adaptive blockage avoidance
Buffer insertion is a fundamental technology for VLSI interconnect optimization. Several existing buffer insertion algorithms have evolved from van Ginneken's classic algorithm. In this work, we extend van Ginneken's algorithm to handle blockages in the layout. Given a Steiner tree containing a Steiner point that overlaps a blockage, a local adjustment is made to the tree topology that enables additional buffer insertion candidates to be considered. This adjustment is adaptive to the demand on buffer insertion and is incurred only when it facilitates the maximal slack solution. This approach can be combined with any performance-driven Steiner tree construction. The overall time complexity has linear dependence on the number of blockages and quadratic dependence on the number of potential buffer locations. Experiments on several large nets confirm that high-quality solutions can be obtained through this technique with little CPU cost.
28075982
Clock buffer polarity assignment for power noise reduction
10406726
Associative Skew Clock Routing for Difficult Instances
In clock network synthesis, sometimes skew constraints are required only within certain groups of clock sinks and do not exist between different groups. This is the so-called associative skew clock routing problem. Although the number of constraints is reduced, the problem becomes more difficult to solve due to the enlarged solution space. The perhaps only previous work used a very primitive delay model and cannot handle difficult instances in which sink groups are intermingled. We reuse existing techniques to solve this problem, including the difficult instances, based on a more accurate and popular delay model. Experimental results show that our algorithm can reduce the total clock routing wirelength by 12% on average compared to greedy-DME which is one of the best zero skew routing algorithms
7189153
Antenna avoidance in layer assignment
The sustained progress of very-large-scale-integration (VLSI) technology has dramatically increased the likelihood of the antenna problem in the manufacturing process and calls for corresponding considerations in the routing stage. In this paper, the authors propose a technique that can handle the antenna problem during the layer-assignment (LA) stage, which is an important step between global routing and detailed routing. The antenna-avoidance problem is modeled as a tree-partitioning problem with a linear-time-optimal-algorithm solution. This algorithm is customized to guide antenna avoidance in the LA stage. A linear-time optimal jumper-insertion algorithm is also derived. Experimental results on benchmark circuits show that the proposed techniques can lead to an average of 76% antenna-violation reduction and 99% via-violation reduction.
11408697
Path-Based Buffer Insertion
Along with the progress of very-large-scale-integration technology, buffer insertion plays an increasingly critical role on affecting circuit design and performance. Traditional buffer insertion algorithms are mostly net based and therefore often result in suboptimal delay or unnecessary buffer expense due to the lack of global view. In this paper, we propose a novel path-based-buffer-insertion (PBBI) scheme which can overcome the weakness of the net-based approaches. We also discuss some potential difficulties of the PBBI approach and propose solutions to them. A fast estimation on buffered delay is employed to improve the solution quality. Gate sizing is also considered at the same time. Experimental results show that our method can efficiently reduce buffer/gate cost significantly (by 71% on average) when compared to traditional net-based approaches. To the best of our knowledge, this is the first work on path based buffer insertion and simultaneous gate sizing.
1271597
An improved AMG-based method for fast power grid analysis
The continuing VLSI technology scaling leads to increasingly significant power supply fluctuations, which needs to be modeled accurately in circuit design and verification. Meanwhile, the huge size of power grid requires its analysis to be fast and highly scalable. Algebraic multigrid (AMG) has been recognized as a promising approach for fast power grid analysis. We propose several techniques to improve AMG-based power grid analysis: (1) dynamic reduction threshold; (2) weighted interpolation; and (3) a new error smoothing scheme. Experimental results on power grid with up to 1.6 million nodes show that these techniques can improve accuracy by over 10 times compared to a reported industrial method while retaining the same fast speed
5955353
Conjectural Equilibrium in Multiagent Learning
Learning in a multiagent environment is complicated by the fact that as other agents learn, the environment effectively changes. Moreover, other agents' actions are often not directly observable, and the actions taken by the learning agent can strongly bias which range of behaviors are encountered. We define the concept of a conjectural equilibrium, where all agents' expectations are realized, and each agent responds optimally to its expectations. We present a generic multiagent exchange situation, in which competitive behavior constitutes a conjectural equilibrium. We then introduce an agent that executes a more sophisticated strategic learning strategy, building a model of the response of other agents. We find that the system reliably converges to a conjectural equilibrium, but that the final result achieved is highly sensitive to initial belief. In essence, the strategic learner's actions tend to fulfill its expectations. Depending on the starting point, the agent may be better or worse off than had it not attempted to learn a model of the other agents at all.
11197335
Traffic grooming, routing, and wavelength assignment in optical WDM mesh networks
In this paper, we consider the traffic grooming, routing, and wavelength assignment (GRWA) problem for optical mesh networks. In most previous studies on optical mesh networks, traffic demands are usually assumed to be wavelength demands, in which case no traffic grooming is needed. In practice, optical networks are typically required to carry a large number of lower rate (sub-wavelength) traffic demands. Hence, the issue of traffic grooming becomes very important since it can significantly impact the overall network cost. In our study, we consider traffic grooming in combination with traffic routing and wavelength assignment. Our objective is to minimize the total number of transponders required in the network. We first formulate the GRWA problem as an integer linear programming (ILP) problem. Unfortunately, for large networks it is computationally infeasible to solve the ILP problem. Therefore, we propose a decomposition method that divides the GRWA problem into two smaller problems: the traffic grooming and routing problem and the wavelength assignment problem, which can then be solved much more efficiently. In general, the decomposition method only produces an approximate solution for the GRWA problem. However, we also provide some sufficient condition under which the decomposition method gives an optimal solution. Finally, some numerical results are provided to demonstrate the efficiency of our method.
13136257
An agent-based architecture for distributed interfaces and timed media in a storytelling application
This paper presents an agent-based architecture that enables the presentation of interactive timed media to distributed and networked devices. The architecture provides an automatic mapping of the same document to different environments. It uses a hierarchy of PAC (Presentation-Abstraction-Control) agents, which allows separating the presentation aspects from the control aspects and dynamically registering the new PAC agents to the system.
9574067
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organized into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness individuals through promotion to higher levels.
4069319
Attribute Reduction Based on Granular Computing
Attribute reduction is a very important issue in data mining and machine learning. Granular computing is a new kind of soft computing theory. A novel method for encoding granules using bitmap technique is proposed in this paper. A new attribute reduction method based on granular computing is also developed with this encoding method. It is proved to be efficient.