Post on 11-Jan-2016
Placement by Simulated Annealing
Simulated Annealing
Simulates annealing process for placement Initial placement
− Random positions
Perturb by block exchanging If Δcost < 0, acceptotherwise, may still accept if const increase is not
too much− Too much increase?− Depends on the algorithm stage
2
3
Search Space
Cost
Avoids getting trapped in local minimum
4
Input: set of all cells VOutput: placement P
T = T0 // set initial temperature
P = PLACE(V) // arbitrary initial placementwhile (T > Tmin)
while (!STOP()) // not yet in equilibrium at T new_P = PERTURB(P) Δcost = COST(new_P) – COST(P) if (Δcost < 0) // cost improvement P = new_P // accept new placement else // no cost improvement r = RANDOM(0,1) // random number [0,1) if (r < e -Δcost/T) // probabilistically accept P = new_P T = α ∙ T // reduce T, 0 < α < 1
Simulated Annealing – Algorithm
Temperature Reduction Function
0
10000
20000
30000
40000
1 51 101 151 201 251 301 351 401
Tem
per
atu
re
5
Cost Decrease
0
200
400
600
800
1 51 101 151 201 251 301 351 401
Co
st F
un
ctio
n
6
SA Parameters
• Quality of result depends largely on parameters:
Initial Temperature Final Temperature Stop Criterion Cooling Schedule Move (Perturb) Function Cost Function
7
VPR Package
V. Betz and J. Rose, “VPR: A new packing, placement and routing tool for FPGA research,” International Workshop on Field-Programmable Logic and Applications, 1997.
J. Luu, I. Kuon, P. Jamieson, T. Campbell, A. Ye, M. Fang, and J. Rose, “VPR 5.0: FPGA CAD and architecture exploration tools with single-driver routing, heterogeneity and process scaling,” in Int’l Symp. on Field-Programmable Gate Arrays, Feb. 2009, pp. 133–142.
VPR 5.0, http://www.eecg.utoronto.ca/vpr. VPR 6.0, Beta Release, May 30, 2011
8
Placement can Make A Difference
MCNC Benchmark circuit e64 (contains 230 4-LUT). Placed to a FPGA.
Random InitialPlacement
FinalPlacement
After DetailedRouting
9
TimberWolf Package
Based on simulated annealing Handles both placement and routing
• Design styles: gate-array, standard-cell macrocell
10
TimberWolf Gate-Array Placement
• State: An assignment of the gates to the gate positions.
• Move Interchange 2 gates, OR Move a gate to a vacant position.
• Cost Function Total wire length estimation Congestion
11
Cooling Schedule Initial temperature: 4x106
Final temperature: 1
12
0.8
0.8
0.95
IToolswww.internetcad.com
Variable die
33% smaller
Translator, Floorplanner, Standard Cell Placer, Gate Array Placer, Global Router & Detail Router
13
TimberWolf for Standard Cell
• Perturbs:MoveSwapMirror around y-axis
14
Range LimiterWhat pairs of modules can be
interchanged?• Range Limiter for Move:
At the beginning, (WT, HT) is very large
− big enough to contain the whole chip.
Window size shrinks slowly as the temperature decreases.
− Height and width log(T).
Stage 2: window size is so small− No inter-row module interchanges
are possible.
15
TimberWolf for Standard Cell
• Stage 1 (at high temperature): Move cells between different rows as well as
within the same row.Allow overlapping of cells.Minimize the total wire length, cell overlapping,
and differences in row lengths.
• Stage 2 (at low temperature):Remove overlapping from the solution of Stage 1.Exchange adjacent cells.Minimize total wire length.
16
Cost Function
),(2 2 jioverlapCostcellsji
rowsi
ithDesRowLengithActRowLengCost |)()(|3
))()()()((1
netsi
iVweightiyspaniHwieghtixspanCost
xspan
yspan
17
18
• Advantages: Can find global optimum (given sufficient time) Well-suited for detailed placement
• Disadvantages: Very slow To achieve high-quality implementation, laborious parameter tuning
is necessary Randomized, chaotic algorithms - small changes in the input
lead to large changes in the output
• Practical applications of SA: Small placement instances with complicated constraints Detailed placement, where SA can be applied in small windows FPGA layout, where complicated constraints
Simulated Annealing
Tabu Search
From a solution s, find a set of solutions in this neighborhood N(s).
• Tabu List (T): Recently tried movements
− To avoid loop. Select a solution from N(s) – T with minimum cost.
19
Probabilistic Congestion Estimation
M. Saeedi, M. Saheb Zamani, A. Jahanian, “Evaluation, prediction and reduction of routing congestion,” Microelectronics Journal, Elsevier, 2007.
20
Channelless Standard Cell
21
Probabilities
The probability of a v-bend path from bin (i, j) to bin (n,m) to pass through the horizontal/vertical track (x, y).
22
Stataistics
23
Congestion Estimation
The number of all possible paths from bin (i,j) to bin (n,m) which pass through the horizontal/vertical track in bin (x,y)
),(
),(
5),(
),(
5
),(
),(
),(
4),(
),(
4
),(
),(
),(
3),(
),(
3
),(
),(
),(
2),(
),(
2
),(
),(
),(
1),(
),(
1
),(
),(
),(),(
**05.0**10.0
**19.0**37.0**29.0
mn
ji
mn
jiyx
mn
ji
mn
jiyx
mn
ji
mn
jiyx
mn
ji
mn
jiyx
mn
ji
mn
jiyx
mn
jiyx
NPNP
NPNPNP
N
),(),(mnjiN : The number of all possible paths
from bin (i,j) to bin (n,m)24
Congestion Map
25