Simulated Annealing. 2 Charactersictics: –Iterative improvement –Begins with an initial (arbitrary) solution and seeks to incrementally improve the objective.

Simulated Annealing

2

Simulated Annealing

• Charactersictics:– Iterative improvement– Begins with an initial (arbitrary) solution and seeks

to incrementally improve the objective function.– During each iteration, a local neighborhood of the

current solution is considered.• A new candidate solution: a small perturbation of

the current solution.– Unlike greedy algorithms, SA algorithms can accept

candidate solutions with higher cost.

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3

Simulated Annealing

Solution states

CostInitial solution

Local optimum

Globaloptimum

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Simulated Annealing

• What is annealing?– Definition (material science):

• Controlled cooling process of high-temperature materials to modify their properties.

– Cooling changes material structure from being highly randomized (chaotic) to being structured (stable).

– The way that atoms settle in low-temperature state is probabilistic in nature.

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Simulated Annealing

• What is annealing?

– Slower cooling: a higher probability of achieving a perfect lattice with minimum-energy Cooling process occurs in steps• Atoms need enough time to try different structures

– Sometimes atoms may create (intermediate) higher-energy states

– Probability of the accepting higher-energy states decreases with temperature

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Simulated Annealing

• Characteristics

• Avoids getting trapped in local minima

• Initial state available

• Improvements by changing floorplan (e.g., exchanging)

• Moves which decrease cost are accepted directly

• Cooling Schedule

• Moves which increase cost are accepted depending on

•T and cost increase

• One of the most common algorithms in floorplanning

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Simulated Annealing AlgorithmInput: initial solution init_solOutput: optimized new solution curr_sol

T = T0 // initializationi = 0curr_sol = init_solcurr_cost = COST(curr_sol)while (T > Tmin) while (stopping criterion is not met) i = i + 1

trial_sol = TRY_MOVE(curr_sol) // try small local change trial_cost = COST(trial_sol) cost = trial_cost – curr_cost if (cost < 0) // if there is improvement, curr_cost = trial_cost // update the cost and curr_sol = MOVE(curr_sol) // execute the move else r = RANDOM(0,1) // random number [0,1] if (r < e –Δcost/T) // if it meets threshold, curr_cost = trial_cost // update the cost and curr_sol = MOVE(curr_sol) // execute the move T = α ∙ T // 0 < α < 1, T reduction

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Temperature Reduction Function

0

10000

20000

30000

40000

1 51 101 151 201 251 301 351 401

Tem

per

atu

re

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Cost Decrease

0

200

400

600

800

1 51 101 151 201 251 301 351 401

Co

st F

un

ctio

n

10

Accepted Moves

0

50

100

150

200

250

1 51 101 151 201 251 301 351 401

Nu

m M

ove

s A

cc

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SA Parameters

• Quality of results, highly dependent on parameter values

• Initial Temperature

• Final Temperature

• Inner Loop Criterion

• Cooling Schedule

• Move Function

• Cost Function

Floorplanning Representation:Sequence-Pair

Floorplanning Representation:Sequence-Pair

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Positive Locus and Negative LocusPositive Locus

of Block b

Negative Locusof Block b

Positive Locus: up-right step-line:

-Starts to move upward.

-Turns direction alternatively right and up until reaching upper right corner without crossing:

i) boundaries of other modules, and

iii) the boundary of the chip.

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Sequence-Pair

Positive Loci Negative Loci

Sequence-Pair = (abdecf, cbfade)

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Geometric Info of Sequence-Pair

Given a floorplan and the corresponding sequence-pair

(P, N):• x is left of y iff x is before y in both P and N.

• x is above y iff x is before y in P and after y in N.

Sequence-Pair = (abdecf, cbfade)

…(x…y…, …x…y)…

yx

…(x…y…, …y…x)…

x

y

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From Sequence-Pair to Relative Positions

• Given a sequence-pair, the floorplan with smallest area can be found in O(n2) time.

• Algorithms of time O(n log log n) or O(n log n) exist. But faster than O(n2) algorithm only when n is quite large.

Labeled grid for(abdecf, cbfade)

ab

de

cf

cb

fa

de

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From Sequence-Pair to Floorplan

• Distance from left (bottom) edge can be found using the longest path algorithm on the horizontal (vertical) constraint graph (HCG, VCG).

Horizontal Constraint Graph Vertical Constraint Graph

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Sequence Pair (SP)

A floorplan is represented by a pair of permutations of the module names:

e.g. 1 3 2 4 5 3 5 4 1 2

A sequence pair (s1, s2) of n modules can represent all possible floorplans formed by the n modules by specifying the pair-wise relationship between the modules.

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Example

Consider the sequence pair:

(13245,41352 )

2

54

1 3

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Floorplan Realization

• Floorplan realization:– Constructs a floorplan from its representation (sequence pair).

• Makes use of HCG and VCG (Gh and Gv).

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Floorplan Realization

• Whenever we see (…A…B…, …A…B…), add an edge from A to B in Gh with weight wA.

• Whenever we see (…A…B…, …B…A…), add an edge from B to A in Gv with weight hA.

• Add a source vertex s (weight = 0) to Gh and Gv pointing to all vertices without incoming edges.

• Find the longest paths from s to every vertex in Gh and Gv (how?), which are the coordinates of the lower left corner of the module in the packing.

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Example

2

54

1 3

(13245,41352 )

2

1.2

1

1.1 1

1.2

2

2.4 1.2

1

3 2

5

4

1.21.2

1.2

1.1

1.1

2.4s

0

0

Gh

1

3 2

5

4s

0 0

Gv

11 1

2

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Example

2

54

1 3

(13245,41352 )

2

1.2

1

1.1 1

1.2

2

2.4 1.2

1

3 2

5

4

1.21.2

1.2

1.1

1.1

2.4s

0

0

Gh

1

3 2

5

4s

0 0

Gv

11 1

2

• Need to remove the transitive edges

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Moves

• Three kinds of moves in the annealing process:M1: Rotate a module, or change the shape of a

module

M2: Interchange 2 modules in both sequences

M3: Interchange 2 modules in the first sequence

• Does this set of move operations ensure reachability?

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Pros and Cons of SP

• Advantages:– Simple representation– All floorplans can be represented.– The solution space is finite. (How big?)

• Disadvantages:– Redundant representation. The representation is not 1-to-1.– The size of the constraint graphs, and thus the runtime to

construct the floorplan is quadratic

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• Initial SP: SP1 = (17452638, 84725361)– Dimensions: (2,4), (1,3), (3,3), (3,5), (3,2), (5,3), (1,2), (2,4)

– Based on SP1 we build the following table:

Sequence Pair Representation

Right of: the list of modules at the right of the module

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Constraint Graphs

• Horizontal constraint graph (HCG)– Before and after removing transitive edges

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Constraint Graphs (cont)

• Vertical constraint graph (VCG)

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Computing Chip Width and Height• Longest source-sink path length in:

– HCG = chip width, VCG = chip height– Node weight = module width/height– Dimensions: (2,4), (1,3), (3,3), (3,5), (3,2), (5,3), (1,2), (2,4)

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Computing Module Location

• Use longest source-module path length in HCG/VCG– Lower-left corner location = source to module input path

length

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Final Floorplan

• Dimension: 11 × 15

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Move I (M3)

• Swap 1 and 3 in positive sequence of SP1

– SP1 = (17452638, 84725361)

– SP2 = (37452618, 84725361)

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Constraint Graphs

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Constructing Floorplan

• Dimension: 13 × 14

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Move II (M2)

• Swap 4 and 6 in both sequences of SP2

– SP2 = (37452618, 84725361)

– SP3 = (37652418, 86725341)

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Constraint Graphs

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Constructing Floorplan

• Dimension: 13 × 12

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Summary

• Impact of the moves:– Floorplan dimension changes from 11 × 15 to 13 × 14 to 13 × 12