1. A SimPLR Method for Routability-driven Placement
Myung-Chul Kim†, Jin Hu†, Dong-Jin Lee and Igor L. Markov Dept. of Computer Science and Engineering (CSE) Dept. of Electrical Engineering and Computer Science (EECS) University of Michigan †equal contribution
Hi, my name is Jin, and today, I’ll be talking about ‘A SimPLR Method for Routability-driven Placement”. This was work done in collaboration with Myung-Chul Kim, Dong-Jin Lee, and Professor Markov at the University of Michigan.
International Conference on CAD (ICCAD) 2011

2. Placement and Routing Objectives
Goal of placement and routing: produce a legal, routable and high-quality solution
No overlapping objects
No (or very few) violations
Small interconnect length
Functionality
Performance (timing, yield, power)
Within the context of placement and routing, the goal is to produce a legal and routable solution with small (or low) routed interconnect length, as these metrics directly affect the final design’s performance and functionality. However, due to the nature and difficulty of this problem, the placement and routing steps were developed separately, where placement optimized for half-perimeter wirelength, subject to no cell overlaps, and in turn, that solution is given to routing, where it optimizes for routed wirelength subject to no violations.
<click>
However, as you may observe, with this current setup, unlike wirelength or solution quality, the burden of routability falls primary with the router, as there’s no explicit or implicit metric within placement.
HPWL-driven Placement
Routing
optimize: half-perimeter WL
optimize: routed WL
subject to: no cell overlaps ?
subject to: no violations
International Conference on CAD (ICCAD) 2011

3. Routability-driven Placement
Issue: Routability becoming harder due to technology advances
Routing blockages
Increased number of metal layers
layer-route requirements
Issue: Placer can harm routability; low HPWL regions can be hard to route
This is a problem, for a couple of reasons. First, just from the router’s perspective, routability is becoming much more difficult due to technology constraints, such as routing blockages, increased number of routing layers, and specific wire-width and spacing issues.
Second, because the placer is not congestion or routability-aware, the solutions they produce can actually harm routability. As we’ve witnessed from past ISPD routing contests, regions with low HPWL can be difficult or impossible to route.
HPWL-driven Placement
Routing
optimize: half-perimeter WL
optimize: routed WL
subject to: no cell overlaps ?
subject to: no violations
International Conference on CAD (ICCAD) 2011

4. Routability-driven Placement
Issue: Routability becoming harder due to technology advances
Routing blockages
Increased number of metal layers
layer-route requirements
Issue: Placer can harm routability; low HPWL regions can be hard to route
routability information is given to placer
If instead, however, the placer is given some congestion information, then we can perform routability-driven placement, and the burden of routability can be shifted to between the router and placer.
That said, we can perform routability-driven placement by building on top of the standard placement flow.
Routability-driven Placement
Routing
optimize: half-perimeter WL
optimize: routed WL
subject to: no cell overlaps
consider: routability
subject to: no violations
International Conference on CAD (ICCAD) 2011

5. Standard Placement Flow
Placement Instance
Global Placement Iteration(s)
So, starting with the standard placement flow, instead of just letting the global placer iterate until some convergence factor solely based on HPWL, <next slide> no
Converge?
yes
Legalization
Detailed Placement
International Conference on CAD (ICCAD) 2011

6. Routability-driven Placement Flow
Placement Instance
Early routability prediction – ability for placer to respond early and often
Initial Global Placement
Congestion Estimation
Must be scalable (speed) and accurate (quality)
Routability-driven
Must know how to use routability information
Global Placement Iteration(s)
First, we generate an initial global placement solution, and estimate its routability or congestion, and then use that information to guide future global placement iterations.
This approach is great for routability, as now because we have early routability prediction, the placer can now respond early and often to handle congestion. However, there remains a few requirements to have this advantage. First, the congestion or routability must be fast and it must be accurate so as to not mislead the placer. Second, given accurate congestion information, the placer must know how to use this knowledge effectively so as to alleviate congestion but also preserve solution quality. no
Converge?
yes
Legalization
Routability components
Congestion-aware
optional
Detailed Placement
International Conference on CAD (ICCAD) 2011

7. Prior Work Congestion Estimation Approaches
Static
Rent’s Rule, pin density
Probabilistic
Probabilistic wire routing, wire density
Constructive
2-d A* routing, fast global router
Give the placer advance, firsthand access to tentative routes and account for technology constraints
Congestion-driven Optimizations in Placement
During Global Placement
Cell bloating, growing/shrinking regions
After Global Placement
Whitespace injection, linear programming
During Detailed Placement
Congestion-based objective function
In terms of previous work, there have been many well-documented approaches and techniques, both in congestion estimation and for optimizations within placement.
For congestion estimation, traditionally, the two most common approaches are classified as either static or probabilistic. More recently, constructive approaches have been used, where real routes have indicated routability. Within placement, techniques have been introduced during global placement, after global placement, and during detailed placement.
Our approach is to incorporate these past ideas, either embedded or not in state-of-the-art tools, and integrate them together in a succinct framework.
<click>
Namely, when we do our congestion, we choose to use a constructive approach by using a global placer, so that the placer can have first-hand access to tentative routes, and to account for routing-based technology constraints.
In terms of placement optimizations, we make contributions both during global placement and during detailed placement.
Incorporate past ideas with new techniques in integrated framework
International Conference on CAD (ICCAD) 2011

8. Congestion Estimation
SimPLR Flow
Initial Wirelength Optimization
Placement Instance
Lookahead Legalization (LAL)
Linear System Solver (CG)
Initial Global Placement
Converge? no
Pseudonet Insertion
from congestion estimation
Congestion Estimation
yes
Global placement iteration
Routability-driven
Global Placement Iteration(s)
Lookahead Routing (LAR)
So, starting with the routability-driven placement flow, we choose to incorporate three major tools.
<click>
For our global placer, we use SimPL; for our global router, we use BFG-R; and for our detailed placer, we use FastPlace-DP. We chose these tools for two primary reasons. First, because in their respective contexts, they have consistently produced high-quality solutions within a reasonable runtime, and second because of their robustness and modularity in terms of integration.
So, starting off,
We use SimPL to generate our initial global placement by the following. First, we do an initial wirelength optimization, and then we perform several iterations of global placement. Namely,
We do lookahead legalization, pseudonet insertion, and then produce a quadratic placement by CG. After we have an initial global placement solution,
We perform congestion estimation by invoking lookahead routing. Once we have the congestion information, we perform routability-driven cell bloating, and then dynamically adjust the target density to preserve solution quality (and I’ll talk about these in subsequent slides).
Then, once we have generated a final global placement solution, we perform legalization, and do
Congestion-aware detailed placement.
So now, I’ll walk through our contributions in chronological order, starting with lookahead routing. no
Converge?
Routability-driven Cell Bloating
Dynamically-adjusting Target Density
yes
Legalization
Global Placer: Global Router: Detailed Placer:
SimPL BFG-R FastPlace-DP
Routability components
Congestion-aware
Placer-specific components
Detailed Placement
International Conference on CAD (ICCAD) 2011
Contributions

9. Lookahead Routing (LAR)
Purpose: provides accurate and fast routability information: interconnect length and congestion (based on actual routes)
Compatibility: routing grid (3-d) versus placement grid (2-d)
V layer
VIA
H layer
placement bin
Part 1: improve runtime without sacrificing accuracy
Part 2: routing congestion (per edge) to placer congestion (per bin)
International Conference on CAD (ICCAD) 2011

10. Lookahead Routing (LAR)
Part 1
usage and capacity of 2.5-d edges
Convert to 2.5-d grid
BFG-R-based routing
3-d routing instance (with raw capacities)
MST construction and simplified RRR
Part 1: improve runtime without sacrificing accuracy
Part 2: routing congestion (per edge) to placer congestion (per bin)
International Conference on CAD (ICCAD) 2011

11. Lookahead Routing (LAR)
Part 2
usage and capacity of 2.5-d edges
congestion of bins
Convert to 2.5-d grid
Convert to 2-d grid
placement bin
BFG-R-based routing
3-d routing instance (with raw capacities)
MST construction and simplified RRR
Report congestion to global placement
Part 1: improve runtime without sacrificing accuracy
Part 2: routing congestion (per edge) to placer congestion (per bin)
International Conference on CAD (ICCAD) 2011

12. LAR: Single 3-d Layer (No Blockages)
For every routing layer l, given wire width ww(l), wire spacing ws(l)
Layer l
International Conference on CAD (ICCAD) 2011

13. LAR: Single 3-d Layer (No Blockages)
For every routing layer l, given wire width ww(l), wire spacing ws(l)
For every routing edge e, given edge capacity cap(e) e
Layer l
For all edges e, define:
normalized capacity nCap(e) =
cap(e) ww(l)+ ws(l)
International Conference on CAD (ICCAD) 2011

14. LAR: Single 3-d Layer (With Blockages)
For every routing edge e, given edge size s(e)
Blockages take up proportionate amount of capacity (based on s(e))
free interval f
routing blockage
s(e)
For all edges e, define:
Free intervals (ranges) f(e) with no routing blockage
normalized capacity nCap(e) =
cap(e) ww(l)+ ws(l) Σ f
f(e) s(e)
International Conference on CAD (ICCAD) 2011

15. LAR: Single 3-d Layer Example
Given: ww(l) = 2, wire spacing ws(l) = 2, cap(e) = 40, s(e) = 50 25 12 8 50
For all edges e, define:
Free intervals (ranges) f(e) where no routing blockage
normalized capacity nCap(e) =
cap(e) ww(l)+ ws(l) Σ f
f(e) s(e)
International Conference on CAD (ICCAD) 2011

16. LAR: Single 3-d Layer Example
Given: ww(l) = 2, wire spacing ws(l) = 2, cap(e) = 40, s(e) = 50 25 12 8 50
50 50
edge A
nCap(A) =
= 10
For all edges e, define:
Free intervals (ranges) f(e) where no routing blockage
normalized capacity nCap(e) =
cap(e) ww(l)+ ws(l) Σ f
f(e) s(e)
International Conference on CAD (ICCAD) 2011

17. LAR: Single 3-d Layer Example
Given: ww(l) = 2, wire spacing ws(l) = 2, cap(e) = 40, s(e) = 50 25 12 8 50
50 50
edge A
nCap(A) =
= 10
0 50
nCap(B) =
= 0
edge B
For all edges e, define:
Free intervals (ranges) f(e) where no routing blockage
normalized capacity nCap(e) =
cap(e) ww(l)+ ws(l) Σ f
f(e) s(e)
International Conference on CAD (ICCAD) 2011

18. LAR: Single 3-d Layer Example
Given: ww(l) = 2, wire spacing ws(l) = 2, cap(e) = 40, s(e) = 50 50 25 12 8
50 50
edge A
nCap(A) =
= 10
edge C
0 50
nCap(B) =
= 0
edge B
25 50
nCap(C) =
= 5
For all edges e, define:
Free intervals (ranges) f(e) where no routing blockage
normalized capacity nCap(e) =
cap(e) ww(l)+ ws(l) Σ f
f(e) s(e)
International Conference on CAD (ICCAD) 2011

19. LAR: Single 3-d Layer Example
Given: ww(l) = 2, wire spacing ws(l) = 2, cap(e) = 40, s(e) = 50 25 12 8 50
50 50
edge A
nCap(A) =
= 10
edge C
0 50
nCap(B) =
= 0
edge B
25 50
nCap(C) =
= 5
edge D
12 50
8 50
nCap(D) = +
For all edges e, define:
Free intervals (ranges) f(e) where no routing blockage
normalized capacity nCap(e) =
= = 3
cap(e) ww(l)+ ws(l) Σ f
f(e) s(e)
International Conference on CAD (ICCAD) 2011

20. International Conference on CAD (ICCAD) 2011
LAR: 3-d Grid to 2.5-d Grid
3-d Routing Grid
2.5-d Routing Grid
International Conference on CAD (ICCAD) 2011

21. International Conference on CAD (ICCAD) 2011
LAR: 3-d Grid to 2.5-d Grid Vk V1 V
3-d Routing Grid
2.5-d Routing Grid
International Conference on CAD (ICCAD) 2011

22. International Conference on CAD (ICCAD) 2011
LAR: 3-d Grid to 2.5-d Grid Hm H1 H
3-d Routing Grid
2.5-d Routing Grid
International Conference on CAD (ICCAD) 2011

23. International Conference on CAD (ICCAD) 2011
LAR: 3-d Grid to 2.5-d Grid eV k eV 1 eV
3-d Routing Grid
2.5-d Routing Grid
nCap(eV ) , … , nCap(eV )
nCap(eV) = nCap(eV ) + … + nCap(eV ) 1 k 1 k
International Conference on CAD (ICCAD) 2011

24. International Conference on CAD (ICCAD) 2011
LAR: 3-d Grid to 2.5-d Grid eH m eH eH 1
3-d Routing Grid
2.5-d Routing Grid
nCap(eV ) , … , nCap(eV )
nCap(eV) = nCap(eV ) + … + nCap(eV ) 1 k 1 k
nCap(eH ) , … , nCap(eH )
nCap(eH) = nCap(eH ) + … + nCap(eH ) 1 m 1 m
International Conference on CAD (ICCAD) 2011

25. After 2.5-d Grid Construction
LAR: 3-d Grid to 2.5-d Grid
After 2.5-d Grid Construction
BFG-R-based routing
MST construction and simplified RRR
3-d Routing Grid
2.5-d Routing Grid
nCap(eV ) , … , nCap(eV )
nCap(eV) = nCap(eV ) + … + nCap(eV ) 1 k 1 k
nUsage(eV) = 1 for every routed net in eV
nCap(eH ) , … , nCap(eH )
nCap(eH) = nCap(eH ) + … + nCap(eH ) 1 m 1 m
nUsage(eH) = 1 for every routed net in eH
International Conference on CAD (ICCAD) 2011

26. International Conference on CAD (ICCAD) 2011
LAR: 2.5-d Grid to 2-d Grid
Routing Grid
Placement Grid
International Conference on CAD (ICCAD) 2011

27. International Conference on CAD (ICCAD) 2011
LAR: 2.5-d Grid to 2-d Grid V P H
Routing Grid
Placement Grid
International Conference on CAD (ICCAD) 2011

28. International Conference on CAD (ICCAD) 2011
LAR: 2.5-d Grid to 2-d Grid
Routing Grid
Placement Grid
top
nUsage(top)
nCap(top)
bottom
nUsage(bottom)
nCap(bottom)
International Conference on CAD (ICCAD) 2011

29. International Conference on CAD (ICCAD) 2011
LAR: 2.5-d Grid to 2-d Grid
Routing Grid
Placement Grid
top
nUsage(top)
nCap(top)
bottom
nUsage(bottom)
nCap(bottom)
nUsage(left)
left
right
nCap(left)
nUsage(right)
nCap(right)
International Conference on CAD (ICCAD) 2011

30. International Conference on CAD (ICCAD) 2011
LAR: 2.5-d Grid to 2-d Grid
top
global placement bin g
left
right
bottom
Cap(g) = nCap(left) + nCap(right) + nCap(top) + nCap(bottom)
Congestion of g
Usage(g) = max(nCap(left), nUsage(left)) max(nCap(right), nUsage(right)) max(nCap(top), nUsage(top)) max(nCap(bottom), nUsage(bottom))
C(g) =
Usage(g) Cap(g)
If at least one of the neighboring edges is congested, then the bin g is considered congested
International Conference on CAD (ICCAD) 2011

31. Routability-driven Cell Bloating
Purpose: coercing cell locations and relieve local routing congestion
For all cells c in congested bin g, cell bloating is based on:
cell properties: connectivity deg(c), size width(c)
progress: history* H(c), congestion C(g)
*Number of times the cell has been in a congested bin
routed solution properties:
design usage
difficulty to route
based on Lookahead Routing Σ g
Usage(g) Cap(g)
r =
relatively insensitive to quality Σ e
OF(e)
nCap(e)
d =
OF(e) = max(0, nUsage(e) – nCap(e))
International Conference on CAD (ICCAD) 2011

32. Routability-driven Cell Bloating
For every (moveable) cell c in congested bin g, bloat by:
Routability-driven Cell Bloating
max(width(c) + 1, 1 + Design(r,d) · H(c) · C(g) · deg(c))
Routing Solution-dependent Function
Design(r,d) = max(0, α · r · d + β)
α and β derived from numerical regressions but not benchmark specific tuning
Incorporate routability properties in classical congestion-driven cell bloating formulation (e.g., CRISP)  based on routed design vs. constant
International Conference on CAD (ICCAD) 2011

33. Dynamically-adjusting Target Density
Purpose: preserves overall solution quality (from cell bloating); encourages cells in uncongested areas to stay close
Trades off between routability and wirelength (quality)
Unnecessarily high target density leads to better HPWL, but may also cause routing failures
Lower target density may increase the overall routed wirelength, which would lead to longer detours.
Finding the best target density remains an open problem
International Conference on CAD (ICCAD) 2011

34. Dynamically-adjusting Target Density
Target density is based on:
design bounding box: area(D)
cells’ bounding box: moveable cells area(Cm), fixed cells area(Cf)
progress: constant φ  increases when LAR reports an increase in WL
Target Density
min(95%, )
area(Cm) area(D) – area(Cf)
+ φ
For uncongested areas, cells do not spread as much
International Conference on CAD (ICCAD) 2011

35. Ex: Lookahead Legalization
Ask MC what benchmark this is from
Lookahead Legalization (LAL) regulates local cell density to meet the given target utilization.
International Conference on CAD (ICCAD) 2011

36. Ex: Lookahead Routing
However, even regular module density doesn’t necessarily lead to uniform routing demand.
And this may lead to routing congestion in local regions. LAR estimates such regions with routing congestion.
Lookahead Routing (LAR) estimates regions with routing congestion.
International Conference on CAD (ICCAD) 2011

37. Ex: Routability-driven Cell Bloating
cells that belong to congested regions are bloated depending on amount of routing congestion in regions and histories of cells,
Cells in congested regions are bloated
International Conference on CAD (ICCAD) 2011

38. Ex: LAL Without Target Density Control
Subsequent LAL shits cells away from congested regions to uncongested regions.
However, w/o target density control, LAL generates placement that maintains the same target utilization even in uncongested regions.
This forces resulting placement to consume more whitespace, rather than shifting cells in congested regions to congested regions.
WL increases.
Subsequent LAL regulates local cell density
International Conference on CAD (ICCAD) 2011

39. Ex: LAL With Target Density Control
Target density control allows more packing in uncongested regions, which effectively encourages cell inflow into congested regions from congested regions.
This limits wirelength impacts due to cell bloating.
Target density control allows more packing in uncongested regions.
International Conference on CAD (ICCAD) 2011

40. Progress of SimPL: SUPERBLUE1218
Roughly Legalized Solution 16
8e+8 14 12
6e+8
HWPL
Scaled Overflow per Bin 10 8
4e+8 6 4
2e+8 2
Legal solution
0e+0 10 20 30 40 50
International Conference on CAD (ICCAD) 2011
Iteration Number
Quadratic Placement

41. Progress of SimPLR: SUPERBLUE1218
Roughly Legalized Solution 16
8e+8 14 12
6e+8
HWPL
Lookahead Routing
Scaled Overflow per Bin 10 8
4e+8 6 4
2e+8 2
Legal solution
0e+0 10 20 30 40 50
International Conference on CAD (ICCAD) 2011
Iteration Number
Quadratic Placement

42. Congestion-aware Detailed Placement
Purpose: preserves (and improves) routability and quality of placement
Follows traditional HPWL-driven DP, but does not hurt routability
Traditional WL-driven detailed placement may pack cells in areas that are difficult to route (congestion-unaware)
Prohibit moves that harm routability
Changing objective function could harm HPWL
Maintains routability  moves cells out of congested regions
International Conference on CAD (ICCAD) 2011

43. Congestion-aware Detailed Placement
Within FastPlace-DP, modify cell swapping to be congestion-aware
For every (moveable) cell c, define:
optimal region R(c)
benefit of swapping with other cell c’ in R(c) bswap
benefit of moving (swapping) with empty space in R(c) bmove
International Conference on CAD (ICCAD) 2011

44. Congestion-aware Detailed Placement
Within FastPlace-DP, modify cell swapping to be congestion-aware
For every (moveable) cell c, define:
optimal region R(c)
benefit of swapping with other cell c’ in R(c) bswap
benefit of moving (swapping) with empty space in R(c) bmove
Condition
Operation
uncongested(c)
&&
uncongested(c’)
bswap ≥ bmove ?
swap(c, c’, bswap > 0) : move(c, R(c), bmove > 0)
International Conference on CAD (ICCAD) 2011

45. Congestion-aware Detailed Placement
Within FastPlace-DP, modify cell swapping to be congestion-aware
For every (moveable) cell c, define:
optimal region R(c)
benefit of swapping with other cell c’ in R(c) bswap
benefit of moving (swapping) with empty space in R(c) bmove
Condition
Operation
uncongested(c)
&&
uncongested(c’)
bswap ≥ bmove ?
swap(c, c’, bswap > 0) : move(c, R(c), bmove > 0)
congested(c)
&&
uncongested(c’)
move(c, R(c))
International Conference on CAD (ICCAD) 2011

46. Congestion-aware Detailed Placement
Within FastPlace-DP, modify cell swapping to be congestion-aware
For every (moveable) cell c, define:
optimal region R(c)
benefit of swapping with other cell c’ in R(c) bswap
benefit of moving (swapping) with empty space in R(c) bmove
Condition
Operation
uncongested(c)
&&
uncongested(c’)
bswap ≥ bmove ?
swap(c, c’, bswap > 0) : move(c, R(c), bmove > 0)
Target density
Bloated cell
congested(c)
&&
uncongested(c’)
move(c, R(c))
uncongested(c)
&&
congested(c’)
swap(c, c’, deg(c) < deg(c’))
International Conference on CAD (ICCAD) 2011

47. Congestion-aware Detailed Placement
Within FastPlace-DP, modify cell swapping to be congestion-aware
For every (moveable) cell c, define:
optimal region R(c)
benefit of swapping with other cell c’ in R(c) bswap
benefit of moving (swapping) with empty space in R(c) bmove
Condition
Operation
uncongested(c)
&&
uncongested(c’)
bswap ≥ bmove ?
swap(c, c’, bswap > 0) : move(c, R(c), bmove > 0)
congested(c)
&&
uncongested(c’)
move(c, R(c))
uncongested(c)
&&
congested(c’)
swap(c, c’, deg(c) < deg(c’))
congested(c)
&&
congested(c’)
C(c) > C(c’) ?
swap(c, c’, deg(c) > deg(c’)) : swap(c, c’, deg(c) < deg(c’))
International Conference on CAD (ICCAD) 2011

48. Empirical Results: SimPL, Contest
SimPL’s target density = 50%
Average are calculated excluding routable benchmarks,
HPWL-driven placement: Average of 3.81x better overflow (7 of 8 best)
at the cost of 4% routed wirelength
Routability-driven placement: Average of 2.04x better overflow (8 of 8 best)
with 1% better routed wirelength
International Conference on CAD (ICCAD) 2011

49. Empirical Total OF Results: ICCAD 2011
Benchmark
Ripple
NTUPlace4*
SimPLR
SUPERBLUE1 90
3222
SUPERBLUE2
730544
670480
740050
SUPERBLUE4
75540
85062
18444
SUPERBLUE5
128054
69820
121894
SUPERBLUE10
646300
583336
567780
SUPERBLUE12
547954
705012
181350
SUPERBLUE15
60222
42362
49286
SUPERBLUE18
38176
9996
21020
Total
1.31x
1.27x
1.0x
SimPL’s target density = 50%
Average are calculated excluding routable benchmarks,
*NTUPlace use 8 processors for cGrip, whereas Ripple and SimPLR use 4
International Conference on CAD (ICCAD) 2011

50. Empirical Results: Ca-DP
Our implementation was written in C++, compiled with g++
4.4.3, and validated on an Intel Core 2 CPU X9650 Linux workstation
running at 3.00GHz. We modified and integrated (i) the SimPL
global placer [21], (ii) the BFG-R global router [17], and (iii) the
FastPlace-DP detailed placer [29].
Maximum Runtime for RRR = 15 min x 4 cores
Average of 18% better overflow (7 of 8 best)
at the cost of 1% in routed wirelength
International Conference on CAD (ICCAD) 2011

51. International Conference on CAD (ICCAD) 2011
Conclusions
New routability-driven placement: SimPLR
Based on fast, effective SimPL placer (ICCAD 2010)
Congestion maps based on actual routes (not probabilistic); this is not too slow
Faithful representation of 3-d routes by 2-d congest. maps
Accurate formulas for cell expansion based on congestion
Preserve solution and ensure stability of global placement
Effective at improving routability
Very strong results on the ISPD 2011 Routability-driven Placement benchmarks
International Conference on CAD (ICCAD) 2011

52. Thank you! Any Questions?
International Conference on CAD (ICCAD) 2011

53. International Conference on CAD (ICCAD) 2011
Backup Slides
International Conference on CAD (ICCAD) 2011

54. Empirical Results: Extended timeouts
International Conference on CAD (ICCAD) 2011

55. Final Product: SUPERBLUE15
ISPD 2011 Routability-driven Contest (Best)
SimPLR
International Conference on CAD (ICCAD) 2011

56. International Conference on CAD (ICCAD) 2011
Conclusions
New routability-driven placement: SimPLR
Integration of global routing into global placement
Congestion-aware detailed placement
Effective at improving routability
Best results on the ISPD 2011 Routability-driven Placement benchmarks
Open problem: target density adjustment
International Conference on CAD (ICCAD) 2011