### Table 4. Experimental results on optimal test access architecture design under power constraints: (a) S 1 (b) S 2 .

"... In PAGE 5: ... G i can be obtained from power models for core i. Experimental results for power-constrained test access archi- tecture design for S 1 and S 2 are shown in Table4 . For our ex- periments, we approximated G i by the number of gates in core i.... In PAGE 5: ... On the other hand, for higher values of W, the testing time is affected substantially. For example, in Table4 (a), for W 24 and power budget of 300 units, the testing time does not decrease with an increase in W due to power constraints. In some cases, the ILP problem may even be infeasible for higher test widths, e.... In PAGE 5: ...g. in Table4 (b) with W =48 and power budget of 300 units for S 2 . Comparing with Table 2, we note that the width distribution is also significantly different due to power constraints.... In PAGE 5: ... This is achieved using a width distribution of (10,10) and test bus assignment (2,2,2,2,2,2,2,2,2,1). However, as seen from Table4 . for test width W =24, the test bus assignment has to be changed to meet power constraints, and the minimum testing time increases to 471900 cycles.... ..."

### Table 4. Experimental results on optimal test access architecture design under power constraints: (a) S1 (b) S2.

2000

"... In PAGE 5: ... Gi can be obtained from power models for core i. Experimental results for power-constrained test access archi- tecture design for S1 and S2 are shown in Table4 . For our ex- periments, we approximated Gi by the number of gates in core i.... In PAGE 5: ... On the other hand, for higher values of W, the testing time is affected substantially. For example, in Table4 (a), for W 24 and power budget of 300 units, the testing time does not decrease with an increase in W due to power constraints. In some cases, the ILP problem may even be infeasible for higher test widths, e.... In PAGE 5: ...g. in Table4 (b) with W = 48 and power budget of 300 units for S2. Comparing with Table 2, we note that the width distribution is also significantly different due to power constraints.... In PAGE 5: ... This is achieved using a width distribution of (10,10) and test bus assignment (2,2,2,2,2,2,2,2,2,1). However, as seen from Table4 . for test width W = 24, the test bus assignment has to be changed to meet power constraints, and the minimum testing time increases to 471900 cycles.... ..."

Cited by 44

### Table 2-(b). Optimal distribution of searching effort

2001

"... In PAGE 9: ... Now we add energy constraints of CQ = 9 and p(i, j) = (i - j)2 to problem (Pa) and generate the original problem (Pf). Optimal solution is shown in Table2 . Although the uniformity strategy still looks effective in the interior of the reachable area of the target, we can also see randomness on the boundary of the area.... In PAGE 10: ...1 0.1 123 45678910 Time Table2 -(a). Optimal probability distribution of target Cells 10 0 0 0 0 0 0 0 0 0 0.... In PAGE 10: ... Table2 -(b) shows that the searcher distributes his searching effort uniformly on almost ... ..."

### Table 2: Technology Mapping results

"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."

### Table 2: Objective functions, constraints and optimization problems

2003

Cited by 4

### Table 3b. Solution Statistics for Model 2 (Minimization)

1999

"... In PAGE 4: ...6 Table 2. Problem Statistics Model 1 Model 2 Pt Rows Cols 0/1 Vars Rows Cols 0/1 Vars 1 4398 4568 4568 4398 4568 170 2 4546 4738 4738 4546 4738 192 3 3030 3128 3128 3030 3128 98 4 2774 2921 2921 2774 2921 147 5 5732 5957 5957 5732 5957 225 6 5728 5978 5978 5728 5978 250 7 2538 2658 2658 2538 2658 120 8 3506 3695 3695 3506 3695 189 9 2616 2777 2777 2616 2777 161 10 1680 1758 1758 1680 1758 78 11 5628 5848 5848 5628 5848 220 12 3484 3644 3644 3484 3644 160 13 3700 3833 3833 3700 3833 133 14 4220 4436 4436 4220 4436 216 15 2234 2330 2330 2234 2330 96 16 3823 3949 3949 3823 3949 126 17 4222 4362 4362 4222 4362 140 18 2612 2747 2747 2612 2747 135 19 2400 2484 2484 2400 2484 84 20 2298 2406 2406 2298 2406 108 Table3 a. Solution Statistics for Model 1 (Maximization) Pt Initial First Heuristic Best Best LP Obj.... In PAGE 5: ...) list the elapsed time when the heuristic procedure is first called and the objective value corresponding to the feasible integer solution returned by the heuristic. For Table3 a, the columns Best LP Obj. and Best IP Obj.... In PAGE 5: ... report, respectively, the LP objective bound corresponding to the best node in the remaining branch-and-bound tree and the incumbent objective value corresponding to the best integer feasible solution upon termination of the solution process (10,000 CPU seconds). In Table3 b, the columns Optimal IP Obj., bb nodes, and Elapsed Time report, respectively, the optimal IP objective value, the total number of branch-and-bound tree nodes solved, and the total elapsed time for the solution process.... ..."

### Table 1: Constraints to optimization problems by difierent sets J J Constraints

### Table 1 and Table 2 contain the results. Problem denotes the problem instance in [2], Fails the number of failures for the overall search (including the proof of optimality), CPU the corresponding runtime in seconds on a Sparc20/70 MHz workstation, and Fails(pr) and CPU(pr) the number of fail- ures and the time needed for the proof of optimality only. For both phases, the distribution strategy described in Sec- tion 4.2 is used. For Table 1, reified constraints were used for the resource constraints2, while for Table 2, edge-finding was used.

1996

"... In PAGE 7: ... Table1 . Results for the Oz Scheduler One surprising observation is that reified constraints for the resource constraints in combination with the used distri- bution strategy are sufficient to obtain good results.... ..."

Cited by 8

### Table 2-(a). Optimal probability distribution of target Cells

2001

"... In PAGE 9: ... Now we add energy constraints of CQ = 9 and p(i, j) = (i - j)2 to problem (Pa) and generate the original problem (Pf). Optimal solution is shown in Table2 . Although the uniformity strategy still looks effective in the interior of the reachable area of the target, we can also see randomness on the boundary of the area.... In PAGE 10: ... Table2 -(b). Optimal distribution of searching effort Cells 10 000 0 0 0 0 0 0 0 9 000 0 0 0 0 0 0 0 8 00 0 0 0 0 0 0.... In PAGE 10: ...123 0.125 12 3 4 5 6 7 8 9 10 Time Table2 -(b) shows that the searcher distributes his searching effort uniformly on almost ... ..."

### Table 1. Number of nodes of optimal schedules for problems with m = 12.

"... In PAGE 12: ... 4.3 Small Problems Table1 shows the number of nodes and the time required for obtaining 10 optimal schedules 4, with the following data : 3 half-days (m = 12), 7 jobs with random durations from 2 to 4 hours and random heights from 1 to 2 persons, real capa = 3, max capa = 5, and 3 randomly generated precedence constraints. Table 1 points out the weak impact of back-propagation from costV ars to starts inside the SoftCumulative.... In PAGE 12: ...3 Small Problems Table 1 shows the number of nodes and the time required for obtaining 10 optimal schedules 4, with the following data : 3 half-days (m = 12), 7 jobs with random durations from 2 to 4 hours and random heights from 1 to 2 persons, real capa = 3, max capa = 5, and 3 randomly generated precedence constraints. Table1 points out the weak impact of back-propagation from costV ars to starts inside the SoftCumulative. On the contrary, when global constraints handle the distribution, propagation among costV ars leads to an important gain in nodes, even for small problems.... ..."