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Congestion Estimation During Top-Down Placement

Congestion Estimation During Top-Down Placement. Xiaojian Yang Ryan Kastner Majid Sarrafzadeh Embedded and Reconfigurable System Lab Computer Science Department, UCLA. Outline. Introduction Motivation Peak Congestion Prediction Regional Congestion Estimation

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Congestion Estimation During Top-Down Placement

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  1. Congestion Estimation During Top-Down Placement Xiaojian Yang Ryan Kastner Majid Sarrafzadeh Embedded and Reconfigurable System Lab Computer Science Department, UCLA 1

  2. Outline • Introduction • Motivation • Peak Congestion Prediction • Regional Congestion Estimation • Experimental Results • Conclusion 2

  3. Introduction • Place & Route Objectives: • Routability and Timing • Placement • Minimizing Bounding Box Wirelength • Shorter Bounding Box  Better Routability • Congestion • Routability problem • Detours --- Timing problem 3

  4. Motivation of Congestion Est. • Early stages of Top-down Placement • Logic design • Congestion Relieving in Top-down Placement 4

  5. Motivation of Congestion Est. • Congestion Relieving based on estimation • White space re-allocation • Moving cells out of congested area 5

  6. Basis of Estimation P = T • B r Rent’s Rule P - Number of external terminals B – Number of cells T – Rent coefficient r – Rent exponent 6

  7. Peak Congestion Estimation --- Worst Case C1 C3 C2 C1 C2 H: # levels 7

  8. Peak Congestion Estimation --- Uniform Distribution C1 8

  9. Peak Congestion Estimation Result 9

  10. Peak Congestion Estimation Result 10

  11. Regional Congestion Est. Internal routing demand External routing demand Uniformly distributed routing supply 11

  12. Internal Routing Estimation • Wirelength Estimation based on Rent’s rule • P = TB • Rent exponent r • Locality of Rent’s rule • Different subcircuits have different Rent Exponents • Rent Exponent Extraction • Dynamic extraction using partitioning tool • Linear regression on data points • Wirelength Estimation Model • Donath’s (1979) and Davis’s (1998) r 12

  13. External Routing Estimation 0.5 0.25 0.5 0.25 0.5 0.25 1.0 Routing demand caused by inter-block connection Probability-matrix within the Bounding box 13

  14. Regional Congestion Est. External Routing demand (routing estimation) + Internal Routing demand (wirelength estimation) = Routing demand (congestion) Of a region 14

  15. Region Congestion Est. Experiments Congestion Estimator Maze Router Compare after Normalization C1 C2 C1’ C2’ C3 C4 C3’ C4’ Top-down Placement 64 x 64 or 128 x 128 15

  16. 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 16

  17. Conclusion & Future Work • Possibility to estimate congestion by Rent’s 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 17

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