### Table 7: Comparison of di erent sizing algorithms when sizing 16-bit buses under 2x-6x minimum pitch- spacing. SISS is single-net interconnect sizing and spacing algorithm applied to multiple nets in a greedy order; GISS/FAF and GISS/VAF are bottom-up dynamic programming algorithms; GISS/EPLR is the algorithm presented in this paper.

1999

"... In PAGE 24: ... The SISS algorithm obtains a local-optimal solution for the GISS problem. We compare the average HSPICE delay for solutions given by these algorithms in Table7 (average delay is our objective function). As seen from the table, the GISS/EPLR algorithm always achieves results better than the SISS solutions, with up to 39% delay reduction.... In PAGE 25: ... However, the capacitance model is no longer monotonically-constrained in the case of large pitch- spacings as shown by cef for center-to-edge spacing = 2:2 m in Figure 2(b). Therefore, the GISS/EPLR obtains better results for large pitch-spacings (5x and 6x cases in Table7 ). Because the GISS/EPLR algorithm is much faster and always achieves the best results, we suggest that the GISS/EPLR algorithm shall be used instead of other algorithms.... ..."

Cited by 9

### Table 6: The SWAB (Sliding Window and Bottom-up) algorithm Using the buffer allows us to gain a semi-global view of the data set for Bottom-Up. However, it important to impose upper and lower bounds on the size of the window. A buffer that is allowed to grow arbitrarily large will revert our algorithm to pure Bottom-Up, but a small

2003

Cited by 16

### TABLE X Interpolated PSNR for Given M in a Fixed M Experiment with Search S = D Top-Down or S = U Bottom-Up.

### TABLE XI Interpolated M for Given PSNR in a Fixed M Experiment with Search S = D Top-Down or S = U Bottom-Up.

### Table 1 shows the algorithm for bottom-up evaluation. The main loop iterates over the set a26 of ground predicates.

2005

"... In PAGE 5: ...Set a26 of ground predicates Example a18a20a3 a8 a6a5a8a7a22a21a23a7a22a24 a11 Algorithm: BottomUpa8 a26 a7 a18 a11 //main loop a25 a1a0 objects in a24 for each a1 a30 a26 do a2 a0 set of all bindings between a25 and a9a14a11 for each a24 a28a31a30 a2 do BUEvaluatora8 a1 a7a22a24 a28 a11 BUEvaluatora8 a1 a7a22a24 a11 //helper procedure a2 a0 set of bindings between a25 and a9a14a11 given a24 for each a24 a28a31a30 a2 do bind a24 a28 to a9a17a11 and evaluate a1 for each a1a66a28a31a30 a15a16a11 do if a1 a28 is learned and a1 improves a1 a28 then a24 a28 a28 a3a0a5a4a7a6a9a8 a10a12a11a14a13a16a15a17a11a14a13a12a18a20a19 a8 a1 a7 a1a66a28 a11 BUEvaluatora8 a1a29a28 a7a22a24 a28 a28 a11 Table1 . Online algorithm for bottom-up evaluation.... ..."

Cited by 2

### Table 1: Performance for parsing, segmentation and detection. The table compares the results for the hierarchial model (without OR nodes) and AND/OR graph with two inference algorithms, i.e. (a) bottom-up only. (b) bottom-up and top-down. Model Testing Size Parsing Segmentation Detection Time

### Table 1. Bottom-up version of the binary algorithm

2002

Cited by 9