Stochastic Physical Synthesis for FPGAs with Pre-routing Interconnect Uncertainty and Process Variat...
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Stochastic Physical Synthesis for FPGAs with Pre-routing Interconnect Uncertainty and Process Variation. Yan Lin and Lei He EE Department, UCLA http://eda.ee.ucla.edu Partially supported by NSF and UC Micro sponsored by Actel. Motivation. Variations Pre-routing interconnect uncertainty

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Yan lin and lei he ee department ucla eda ee ucla

Stochastic Physical Synthesis for FPGAs with Pre-routing Interconnect Uncertainty and Process Variation

Yan Lin and Lei He

EE Department, UCLA

http://eda.ee.ucla.edu

Partially supported by NSF and UC Micro sponsored by Actel


Motivation

Motivation

  • Variations

    • Pre-routing interconnect uncertainty

    • Process variation

  • Impact

    • Any near-critical paths  statistically timing critical

    • STA ignores near-criticality

  • Related work for FPGAs

    • Chipwise placement [Cheng, FPL’06]

    • Stochastic placement [Lin, FPL’06]

    • Stochastic routing [Sivaswamy, FPGA’07]

Stochastic physical synthesis and the interaction have not been studied for FPGAs


Outline

Outline

  • Preliminaries

  • Stochastic Clustering

  • Stochastic Placement

  • Stochastic Routing

  • Interaction between Clustering, Placement and Routing

  • Conclusions


Model of variations

Model of Variations

  • Pre-routing interconnect uncertainty modeled as independent Gaussian distribution

    • Standard deviation estimated with post-routing delay distribution

  • Again, Gaussian models for process variations

    • Threshold voltage (Vth)

    • Effective channel length (Leff)

    • Model these variation sources as independent Gaussians


Model of variations1

Model of Variations

  • Pre-routing interconnect uncertainty modeled as independent Gaussian distribution

    • Standard deviation estimated with post-routing delay distribution

  • Again, Gaussian models for process variations

    • Threshold voltage (Vth)

    • Effective channel length (Leff)

    • Model these variation sources as independent Gaussians

  • Delay with variations

    • First order canonical form

  • models process variation

  • models interconnect uncertainty

  • are standard deviations


Synthesis flow

Synthesis Flow


Synthesis flow1

Synthesis Flow


Synthesis flow2

Synthesis Flow


Synthesis flow3

Synthesis Flow


Synthesis flow4

Synthesis Flow


Experimental settings

Experimental Settings

  • Variation and device setting

    • 10%/10%/6% as 3 sigma for global/spatial/local variation in Vth and Leff

    • IRTS 65nm technology node

  • Island style FPGA architecture

    • Cluster size 10 and LUT size 4

    • 60% length-4 and 40% length-8 wire in interconnects

  • Yield loss in failed parts per 10K parts (pp10K)

    • 2.5 sigma guard-banded delay as the cut-off delay

    • Evaluated using MCNC designs


Outline1

Outline

  • Preliminaries

  • Stochastic Clustering

  • Stochastic Placement

  • Stochastic Routing

  • Interaction between Clustering, Placement and Routing

  • Conclusions


Stochastic clustering st vpack

  • Statistical timing cost of BLE B

Stochastic Clustering ST-VPack

  • Based on T-VPack [Betz, FPGA book]

    • An iterative approach

      • Select a seed BLE for a new cluster

      • Pack BLE into the current cluster

    • STA with constant delay model to calculate slack

  • ST-VPack performs SSTA

    • Statistical criticality of an edge/node is the probability of this edge/node being timing critical with variations

  • With statistical criticality

    • Better seed BLE selection

    • Better candidate BLE selection for the current cluster


The impact of the combination of two uncertainty sources

The Impact of the Combination of Two Uncertainty Sources

  • Timing gain mainly due to modeling interconnect uncertainty

    • Modeling interconnect uncertainty leads to a better delay distribution than process variation

    • Considering both does not have much further gain


Interconnect uncertainty vs process variation in clustering

Interconnect Uncertainty vs.Process Variation in Clustering

  • Clearly, interconnect uncertainty leads to a more significant delay variance in clustering

With process variation

With interconnect uncertainty


Comparison between t vpack and st vpack

Comparison between T-VPack and ST-VPack

  • ST-VPack on average reduces

    • mean delay by 5.0% (up to 13.0%)

    • standard deviation by 6.4% (up to 31.8%)

    • yield loss from 50pp10K to 9pp10K

  • In addition, ST-VPack has virtually no wire length, area and runtime overhead


Outline2

Outline

  • Motivation and Background

  • Stochastic Clustering

  • Stochastic Placement

  • Stochastic Routing

  • Interaction between Clustering, Placement and Routing

  • Conclusions


Pre routing interconnect uncertainty vs process variation in placement

Pre-routing Interconnect Uncertainty vs.Process Variation in Placement

With interconnect uncertainty

With process variation

  • Clearly, process variation leads to a more significant delay variance in placement

    • Only considering process variation is sufficient


Stochastic placement st vplace

Stochastic Placement ST-VPlace

  • Stochastic placement developed in [Lin, FPL’06]

    • Based on T-VPlace [Marquardt, ISFPGA’00]

    • Replace SSTA with STA

    • Replace statistical criticality with static criticality

  • Main improvement

    • Consider spatially correlated variation with PCA


Comparison between t vplace and st vplace

Comparison between T-VPlace and ST-VPlace

  • ST-VPlace on average reduces

    • mean delay by 4.0% (up to 14.2%)

    • standard deviation by 6.1% (up to 22.7%)

    • yield loss from 50pp10K to 12pp10K

    • virtually no wire overhead

  • On the other hand, ST-VPlace takes 3.1X runtime


Outline3

Outline

  • Preliminaries

  • Stochastic Clustering

  • Stochastic Placement

  • Stochastic Routing

  • Interaction between Clustering, Placement and Routing

  • Conclusions


Stochastic routing st pathfinder

  • ST-PathFinder performs SSTA

    • The new statistical cost function for node n is

Stochastic Routing ST-PathFinder

  • Based on PathFinder [Betz, FPGA book]

    • An iterative maze router, w/ congestion allowed

    • Considering both timing and wiring costs

  • Interconnect estimation in routing

    • Occurs when predicting delay to the target sink

    • Has the highest accuracy

  • better tradeoff between timing and wiring costs


Comparison between pathfinder and st pathfinder

Comparison between PathFinder and ST-PathFinder

  • ST-PathFinder on average reduces

    • mean delay by 1.4% (up to 7.8%)

    • standard deviation by 0.7% (up to 5.2%)

    • yield loss from 50pp10K to 35pp10K

    • no runtime overhead

  • ST-PathFinder also reduces wire length by 4.5% on average


Outline4

Outline

  • Preliminaries

  • Stochastic Clustering

  • Stochastic Placement

  • Stochastic Routing

  • Interaction between Clustering, Placement and Routing

  • Conclusions


Interaction between clustering placement and routing

Interaction between Clustering, Placement and Routing

  • The stochastic flow reduces yield loss from 50 to 5, but 3.0X runtime

  • Timing gain mainly due to clustering and placement, but w/ overlap

  • Deterministic clusterer, placer + stochastic router is a good flow

    • Significant wiring gains and less runtime

  • Stochastic clustering + deterministic P&R is a good flow

    • Significant timing gains and slightly less runtime


Conclusions

Conclusions

  • The timing gain mainly due to clusterer and placer

    • modeling interconnect uncertainty for clustering

    • considering process variation for placement

  • The stochastic flow reduces

    • yield loss from 50 to 5pp10K

    • mean delay by 6.2%, standard deviation by 7.5%

    • but takes 3X runtime

  • Deterministic clusterer, placer + stochastic router

    • reduces wire length by 4.5%

    • also runs slightly faster than deterministic flow

  • Stochastic clusterer + deterministic P&R reduces

    • yield loss from 50 to 9pp10K

    • mean delay by 5.0%, standard deviation by 6.4%

    • also runs slightly faster than deterministic flow


Yan lin and lei he ee department ucla eda ee ucla

Thank You!


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