Congestion Estimation During Top-Down Placement

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Congestion Estimation During Top-Down Placement

• Xiaojian Yang Ryan Kastner Majid
  Sarrafzadeh
• Embedded and Reconfigurable System Lab
• Computer Science Department, UCLA

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Outline

• Introduction
• Motivation
• Peak Congestion Prediction
• Regional Congestion Estimation
• Experimental Results
• Conclusion

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Introduction

• Place Route Objectives
• Routability and Timing
• Placement
• Minimizing Bounding Box Wirelength
• Shorter Bounding Box ? Better Routability
• Congestion
• Routability problem
• Detours --- Timing problem

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Motivation of Congestion Est.

• Early stages of Top-down Placement
• Logic design
• Congestion Relieving in Top-down Placement

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Motivation of Congestion Est.

• Congestion Relieving based on estimation
• White space re-allocation
• Moving cells out of congested area

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Basis of Estimation
Rents Rule
P T B r
P - Number of external terminals B Number of
cells T Rent coefficient r Rent exponent
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Peak Congestion Estimation --- Worst Case
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Peak Congestion Estimation --- Uniform
Distribution
C1
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Peak Congestion Estimation Result
Circuit Real max cong. Est. max cong.
Ibm01 31 30.3
Ibm02 67 62.7
Ibm03 62 47.8
Ibm04 52 52.1
Ibm05 90 89.1
Ibm06 60 82.3
Ibm07 90 86.8
Ibm08 100 111.9
Ibm09 75 93.0
Ibm10 112 135.8
Ibm11 50 53.9
Ibm12 76 76.1
Ibm13 108 85.5
ibm14 111 117.6
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Peak Congestion Estimation Result
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Regional Congestion Est.
Internal routing demand
External routing demand
Uniformly distributed routing supply
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Internal Routing Estimation

• Wirelength Estimation based on Rents rule
• P TB
• Rent exponent r
• Locality of Rents rule
• Different subcircuits have different Rent
  Exponents
• Rent Exponent Extraction
• Dynamic extraction using partitioning tool
• Linear regression on data points
• Wirelength Estimation Model
• Donaths (1979) and Daviss (1998)

r
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External Routing Estimation
1.0
Routing demand caused by inter-block connection
Probability-matrix within the Bounding box
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Regional Congestion Est.
External Routing demand (routing estimation)

Internal Routing demand (wirelength estimation)

Routing demand (congestion) Of a region
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Region Congestion Est. Experiments
Top-down Placement
64 x 64 or 128 x 128
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Estimation Result

• 8 benchmarks, 12k cells --- 147k cells
• 2 x 2 regions
• Wirelength Estimation only 9
• Including External Routing demand 8
• 4 x 4 regions
• Wirelength Estimation only 13
• Including External Routing demand 9
• Running time
• Partitioning speed
• 147k cells, 2 x 2, 860 seconds, Sun Ultra-10
• Place / Route 8000 seconds

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Conclusion Future Work

• Possibility to estimate congestion by Rents rule
• Congestion can be estimated during Top-down
  placement
• Peak congestion after L-shape routing can be
  accurately estimated
• Regional congestion estimation is within 10
  comparing with actual congestion by place/route
• Future work
• More accurate model for hot spot estimation
• Fast estimation by Rent parameter prediction