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Circuit-wise Buffer Insertion and Gate Sizing Algorithm with Scalability

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Circuit-wise Buffer Insertion and Gate Sizing Algorithm with Scalability. Zhanyuan Jiang and Weiping Shi. DAC 2008, June 8–13, 2008, Anaheim, California, USA. Outline. Introduction Problem Formulation Algorithm Post-buffering Timing Estimation - PowerPoint PPT Presentation

Text of Circuit-wise Buffer Insertion and Gate Sizing Algorithm with Scalability

  • Circuit-wise Buffer Insertion and Gate Sizing Algorithm with Scalability

    Zhanyuan Jiang and Weiping Shi

    DAC 2008, June 813, 2008, Anaheim, California, USA.

  • OutlineIntroductionProblem FormulationAlgorithmPost-buffering Timing EstimationLinear Modeling of Non-linear Delay vs. Cost TradeoffDynamic Critical Sink SelectionLinear ProgrammingCircuit PartitionExperimental ResultsConclusion

  • OutlineIntroductionProblem FormulationAlgorithmPost-buffering Timing EstimationLinear Modeling of Non-linear Delay vs. Cost TradeoffDynamic Critical Sink SelectionLinear ProgrammingCircuit PartitionExperimental ResultsConclusion

  • IntroductionAs VLSI technology enters the nanoscale regime, a great amount of efforts have been made for timing optimization.Among them, buffer insertion stands out as an effective technique to reduce interconnect delay.Due to technology shrinking, more and more gates are placed on a chip, and algorithms without scalability can not fit into future physical synthesis flow.

  • OutlineIntroductionProblem FormulationAlgorithmPost-buffering Timing EstimationLinear Modeling of Non-linear Delay vs. Cost TradeoffDynamic Critical Sink SelectionLinear ProgrammingCircuit PartitionExperimental ResultsConclusion

  • Problem FormulationWe represent a combinational circuit as a Directed Acyclic Graph (DAG) G = (V,E).

  • Problem FormulationThe paper abstract the routing tree of the circuit and ignore all the details (i.e., Steiner node and interconnect tree structure, etc.) within the routing tree.

    The vertices only represent PI/PO of the circuit and input/output pins of modules while edges only for input-to-output paths within a module.

  • Problem Formulation

  • OutlineIntroductionProblem FormulationAlgorithmPost-buffering Timing EstimationLinear Modeling of Non-linear Delay vs. Cost TradeoffDynamic Critical Sink SelectionLinear ProgrammingCircuit PartitionExperimental ResultsConclusion

  • Post-buffering Timing EstimationA post-buffering timing estimation technique is proposed in [12], which derives delay equations along a buffered wire segment and applies the equations for the delay estimation upon multiple-sink nets.

  • Linear Modeling of Non-linear Delay vs. Cost TradeoffTable 1 shows that the impact of varying downstream sink size from 1X to 4X is negligible at the driver.

  • Linear Modeling of Non-linear Delay vs. Cost Tradeoff

  • Linear Modeling of Non-linear Delay vs. Cost TradeoffA curve fitting method is adopted to approximate each tradeoff as several linear segments. In this paper, the number of segments is set as 2, which gives good accuracy.( If the number of segments is 3, the final circuit Elmore delay improves less than 0.1% while the linear programming solver time increases more than 50%. )

  • Linear Modeling of Non-linear Delay vs. Cost TradeoffQroot + c1Xc + c3 Qsink, (1)Qroot + c2Xc + c4 Qsink, (2)Lc Xc Uc, (3)

    Qroot :RAT values at rootQsink :RAT values at sinkXc :the number of buffers at this netCi :the curve fitting coefficientLc, Uc :lower bound and upper bound of the number of buffers

  • Linear Modeling of Non-linear Delay vs. Cost Tradeoff

  • Dynamic Critical Sink SelectionA multiple-sink net contains sink S1, S2, , Sn, and each sink has corresponding RAT Q1, Q2, , Qn. It is hard to know which is critical sink before the stage of buffer insertion.

    At the root, for a specific buffer number, we select the solution that minimizes the maximum delay among all sinks. Thus, only one delay cost tradeoff curve is returned.

  • Dynamic Critical Sink Selection

  • Dynamic Critical Sink SelectionSolution set 1Solution set 2

  • Dynamic Critical Sink SelectionQroot + c1Xc + c5 Qsinkone, (1)Qroot + c2Xc + c6 Qsinkone, (2)Qroot + c3Xc + c7 Qsinktwo, (3)Qroot + c4Xc + c8 Qsinktwo, (4)Lc Xc Uc, (5)

    Qroot :RAT values at rootQsink :RAT values at sinkXc :the number of buffers at this netCi :the curve fitting coefficientLc, Uc :lower bound and upper bound of the number of buffers

  • Linear Programming

    Ci :the curve fitting coefficientXi :the cost of routing tree RT(i)Lc, Uc :lower bound and upper bound

  • Circuit PartitionThis paper adopt the divide-and-conquer scheme to speed up the algorithm.

    The key components of the circuit partition technique are how to decide partition boundaries and how to set up side inputs/outputs in the sub-circuits.

  • Circuit PartitionIn order to minimize partition error, the technique avoids partitioning the critical paths into different sub-circuits, which means that partition boundaries never cut through the most critical path.

    If there is an overlap between different downstream cones, the overlap part belongs to the cone with the most critical primary input.

  • Circuit Partition

    Figure 5: The circuit is partitioned into three subcircuits based on the downstream cones of primary inputs. The input a is the most critical primary input in the circuit.

  • OutlineIntroductionProblem FormulationAlgorithmPost-buffering Timing EstimationLinear Modeling of Non-linear Delay vs. Cost TradeoffDynamic Critical Sink SelectionLinear ProgrammingCircuit PartitionExperimental ResultsConclusion

  • Experimental Results

  • Experimental Results

  • Experimental Results

  • OutlineIntroductionProblem FormulationAlgorithmPost-buffering Timing EstimationLinear Modeling of Non-linear Delay vs. Cost TradeoffDynamic Critical Sink SelectionLinear ProgrammingCircuit PartitionExperimental ResultsConclusion

  • ConclusionExperiments demonstrate that the circuit-wise algorithm achieves on average 17.4X speedup compared with the path based algorithm.

  • The whole circuit is partitioned into n downstream cones plus the remaining circuit.

    The circuit is partitioned into n + m or n + k sub-circuits depending on whether the remaining circuit is disjointed or not.

  • The problem defined as follows:Given a DAG which represents a placed and routed combinational circuit, possible candidate buffer locations, a buffer library and a gate library, find a buffering and gate sizing solution such that the total cost of buffers and gates are minimized, and the required arrival time at each primary input is less than a given constant constraint.

  • Elmore delayD(e) =R(e)[C(e)/2 + C(vj)]D(vj) = K(b) + R(b) C(vj)

    e: edge (vi, vj)R(e): resistance of eC(e): capacitance of eC(vj): downstream capacitance at vjK(b): intrinsic delay of buffer bR(b): driving resistance of buffer b

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