In this paper, we propose an optimal peer assignment algorithm on peer-to-peer networks. This algorithm is designed to maximize the quality of transmitting fine-scalable coded content by exploiting the embedding prope...
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In this paper, we propose an optimal peer assignment algorithm on peer-to-peer networks. This algorithm is designed to maximize the quality of transmitting fine-scalable coded content by exploiting the embedding property of scalable coding. To be more realistic, we assume that the requesting peer has a delay constraint to display the content within a certain delay bound, and it also has limited incoming bandwidth. We first use a simple example to illustrate the peer assignment problem, and then formulate this problem as a linearprogramming problem, followed by a nonlinearprogramming problem. To efficiently solve the second nonlinear problem, we transform it into a sequence of linear programming problems. Finally, we apply our proposed algorithm to both image and video transmissions in bandwidth-limited environments. Extensive experiments have been carried out to evaluate the complexity and performance of our approach by comparing it with both nonlinear formulation and two heuristic schemes. The results have verified the superior performance of our proposed algorithm.
In this paper, we propose an efficient algorithm to reduce the voltage noises for on-chip power/ground (P/G) networks of VLSI. The new method is based on the sequence of linear programming (SLP) as the optimization en...
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In this paper, we propose an efficient algorithm to reduce the voltage noises for on-chip power/ground (P/G) networks of VLSI. The new method is based on the sequence of linear programming (SLP) as the optimization engine, and partitioning scheme for dealing with large-sized circuits. We show that by directly optimizing the decoupling capacitor (decap) areas as the objective function and using the time-domain adjoint method, SLP can deliver much better quality in terms of decap budget than existing methods based on the merged time-domain adjoint method. The partitioning strategy further improves the scalability of the proposed algorithm and makes it efficient for larger circuits. The resulting algorithm is general enough for any P/G network. Experimental results demonstrate the advantage of the proposed method over existing state-of-the-art methods in terms of solution quality at a mild computation cost increase. (c) 2006 Elsevier B.V. All rights reserved.
We propose a novel and efficient charge-based decoupling capacitance budgeting algorithm. Our method uses the macro-modeling technique and effective radius of decoupling capacitance to reduce the size of the problem. ...
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ISBN:
(纸本)1595933816
We propose a novel and efficient charge-based decoupling capacitance budgeting algorithm. Our method uses the macro-modeling technique and effective radius of decoupling capacitance to reduce the size of the problem. We formulate the nonlinear optimization into a linear program (LP) by integrating the nodal equations across a time period of interest and through certain approximations. To reduce the error caused by linearization, we do multiple iterations of the linear program. Experimental results demonstrate that, with the proposed algorithm, even very large power networks (eg. 5 million nodes) can be optimized in a couple of hours with 1-2 transient analyses. Comparison of our algorithm with another heuristic method shows area efficiency and run time advantage of our method.
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