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Handbook of algorithms for physical design automation part 20

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FIGURE 9.6 Illustration of three types of moves.. in the rest of this chapter. It employs the technique of simulated annealing [17], and uses the set of normalized Polish expressions as the solution space to avoid an unnecessarily large number of states and thus enables the speedup of the search procedure significantly. It is claimed that the three types of...

Handbook of algorithms for physical design automation part 21

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Each of the first n − 1 elements in the array represents an internal node, and the first element always represents the root of the tree. Each element of the array is associated with a ten-tuple (t, p, l, r, x, y, z, w, d, h). Given the static array of a slicing tree, the position of each module and...

Handbook of algorithms for physical design automation part 22

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Add block i from the left of the chip pushing aside top m i blocks, where m i is the number of ‘R’s in I ( i. that are adjacent to the left boundary of the chip.. Add block i from the top of the chip pushing down leftmost m i blocks, where m i is the number of ‘B’s...

Handbook of algorithms for physical design automation part 23

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On fixed points of the composite of commuting functions. Proceedings of the 38th ACM/IEEE Design Automation Conference, Las Vegas, NV, pp. 11.1 Introduction. 11.1.1 Problem Definition. 11.2 O-Tree. 11.2.1 Relationship between a Placement and an O-Tree. 11.2.2 O-Tree Perturbations. 11.3 B ∗ -Tree. 11.3.1 From a Placement to a B ∗ -Tree. 11.3.2 From a B ∗ -Tree to a...

Handbook of algorithms for physical design automation part 24

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For the exchange and insert (rotate and randomize) operations, the first l terms of the given CS will not be changed during perturbation, where l = min { i, j. Figure 11.8 illustrates the procedure to perturb the CS using the exchange operation. If two modules f and h in S 6 and S 8 are exchanged, we have the...

Handbook of algorithms for physical design automation part 25

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Figure 11.18a through f illustrate the procedure to extract a − from the placement of Figure 11.24a. We first extract the module b a on the bottom-left corner (Figure 11.18a), and then b b because it is the left-module with all the modules below b b having been extract (Figure 11.18b). As shown in Figure 11.18b and g, for the...

Handbook of algorithms for physical design automation part 26

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FIGURE 11.28 3D placement. The corresponding 3D-subTCG is given in the Figure 11.29. FIGURE 11.29 Corresponding 3D-subTCG of Figure 11.28. In contrast, T-tree is a partially topological representation and can only represent part of the compacted 3D floorplans where each task must be compacted to the origin.. Because the relation between each pair of tasks is defined in the representation,...

Handbook of algorithms for physical design automation part 27

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12.2.5 M ODULE L OCATIONS: K NOWN OR U NKNOWN?. The whole point of floorplanning is to find suitable locations for modules and so it would seem that this information would be unknown at the start of the process. For example, if a chip is based off of an earlier generation of the same chip, a floorplan architect who was...

Handbook of algorithms for physical design automation part 28

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The width distribution list of the new rectangle is. Including standard deviation in the cost function makes it more likely that the area of the floorplan obtained by statistical floorplanning is close to the area of the final solution (i.e., after module dimensions have all been finalized) relative to minimizing expected area alone. In this section, we discuss a floorplanning-like...

Handbook of algorithms for physical design automation part 29

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While this has some basis in practice, it is not true in theory, if arbitrary rearrangement of the hierarchy is allowed. This can be shown as follows [28]: take the result of the hypothetically more efficient flat tool or procedure. There may be no easy way to express high-level constraints on the block pins. The usual solution is to match...

Handbook of algorithms for physical design automation part 30

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Alpert/Handbook of Algorithms for Physical Design Automation AU7242_C013 Finals Page . 272 Handbook of Algorithms for Physical Design Automation. ACM Transactions on Design Automation of Electronic Systems (TODAES . In ICCAD ’87, Proceedings of the International Conference on Computer-Aided Design 1987, pp. ACM Press, NY, 1987.. In GLSVLSI ’96, Proceedings of the 6th Great Lakes Symposium on VLSI, p. In...

Handbook of algorithms for physical design automation part 31

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A strategy of simple uniform spreading of the design may work well for dense designs, but can unnecessarily hurt timing for sparse designs. Thus, white space management is absolutely required in modern placement algorithms to achieve better timing and routability.. A standard cell based design consists of circuit elements called standard cells whose heights remain the same, and the placement...

Handbook of algorithms for physical design automation part 32

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[43] not only developed a dynamic programming technique to choose optimal cut sequences for partitioning-based placement but also found that nearly optimal cut sequences could be determined from the aspect ratio of the bin to be split. This technique has been independently used in the Capo placer [30–35].. After the cutline direction is chosen, partitioning-based placers generally choose the cut-line...

Handbook of algorithms for physical design automation part 33

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This way, specific locations are determined when the right level of detail is considered (Figure 15.10). 25 Bipartition netlist of the partition. FIGURE 15.9 Modified mincut floorplacement flow. FIGURE 15.10 The plot on the left illustrates traditional floorplacement. Whenever a floorplanning threshold is reached, all macros in the bin are designated for floorplanning. The plot on the right illustrates the...

Handbook of algorithms for physical design automation part 34

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Algorithm simulated_annealing(void). FIGURE 16.1 Basic simulated annealing algorithm.. j ∈ S ( j min ) (16.1) where j min is the local minimum state, and S ( j min ) is the set of states reachable from the state j min. Simulated annealing is such a search strategy.. At the heart of the simulated annealing algorithm is the Metropolis Monte...

Handbook of algorithms for physical design automation part 35

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This resulted in a factor of 2–3 speedup in execution time and a 6–17 percent improvement in half-perimeter wirelength.. They also saw an improvement in half-perimeter wirelength due to clustering although some of the improvement may be due to their new cost function and wire estimation model. In this work, the cost function used only contained the sum of the...

Handbook of algorithms for physical design automation part 36

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The star and the clique model (and any other linear model with fixed topology in the sense of Theorem 2) can be reduced to the bounding box model by adding a cell with a single pin for each auxiliary point and replacing each net equivalently by an appropriate set of two-terminal nets. For any placement of the pins, the weighted...

Handbook of algorithms for physical design automation part 37

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Prescribing the centers of gravity of the cell groups is an efficient way to spread the cells over the chip area. Moreover, the constraints may be too strong if we do not demand an even distribution of the cells. If we allow a higher area utilization in some regions, it will often be reasonable to place cells in their region...

Handbook of algorithms for physical design automation part 38

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2 dx dy (18.8) where r ( x, y ) is the vector representation of position ( x, y. In this method, the continuous integral in Equation 18.8 is replaced with a bin structure based on a Barnes–Hut quad-tree [17] and the forces are computed using a particle–mesh–particle approach.. The magnitudes (or strength) of the constant forces at each iteration...

Handbook of algorithms for physical design automation part 39

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FIGURE 18.7 Illustration of median calculation for a cell C connected to three nets. Because median improvement attempts to reposition each cell within its median rectangle, the use of median improve- ment can reintroduce cell overlap into the placement. FIGURE 18.8 Rectangles used in FDP based on Goto’s idea to improve wirelength. that the algorithm is reintroducing too much cell...