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Chop-SPICE: An Efficient SPICE Simulation Technique For Buffered RC Trees Myung-Chul Kim, Dong-Jin Lee and Igor L. Markov Dept. of EECS, University of Michigan 1TAU 2011, Myung-Chul Kim, University of Michigan
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Fast SPICE Simulation: Motivation ■IC timing closure, especially at advanced technology nodes, heavily depends on highly-accurate timing simulations −Increasing impact of PVT variation −Rigorous clock skew/slew constraints ■Circuit size and complexity rapidly increasing −Scalable SPICE technique is critical 2TAU 2011, Myung-Chul Kim, University of Michigan
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Key Feature of Chop-SPICE ■Developed as a compromise simulator (fast yet sufficiently accurate) for use by Contango2 software in the ISPD 2010 contest ■Simple and practical divide-and-conquer approach ■Can capture PVT variation and spatial correlation ■Flexible trade-off between runtime and solution quality ■Adaptability to various SPICE simulators 3TAU 2011, Myung-Chul Kim, University of Michigan
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ISPD10 Clock Tree Synthesis Contest ■45nm 2GHz CPU benchmarks from IBM and Intel ■Objective: Minimize the overall capacitance of the clock network −Subject to constraints: –Monte-Carlo SPICE simulations with PVT variations –Local clock skew < 7.5 ps –Slew rate < 100ps –Hard runtime limit per benchmark < 12 hours ■Low-skew clock trees are especially unforgiving to timing-analysis inaccuracies 4TAU 2011, Myung-Chul Kim, University of Michigan
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Prior Work 5 TAU 2011, Myung-Chul Kim, University of Michigan ■Ideal Timing Evaluator −Fast runtime without sacrificing accuracy −High fidelity, adaptability to various SPICE tools Speed Accuracy Simulation Elmore, D2M, LnD Ideal Timing Evaluator SPICE, AWE Delay Models
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Chop-SPICE Algorithm ■Definition: Probing Points −Given an RC tree, probing points are defined as A.Input nodes of buffers B.Sink nodes − = Set of probing points − = Number of fanouts to probing points at node s i ■Example 6TAU 2011, Myung-Chul Kim, University of Michigan
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Chop-SPICE Algorithm ■Definition: Granularity −Maximum Granularity: −Minimum Granularity: −Granularity Range: −Target Granularity: ■Target Granularity determines minimum number of probing points to be included in sub-circuits 7TAU 2011, Myung-Chul Kim, University of Michigan
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Chop-SPICE Flow 8 RC Tree instance RC Tree traversal yes no Invoke SPICE simulation Target granularity reached? RC tree exhausted? Sub-circuit generation Apply input slew stimuli Delay and slew propagation no yes Delay and slew update End
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Sub-circuit Generation ■Sub-circuits are always delimited by buffers −If a probing point is an input node of buffer(s), all fanout buffers are explicitly included in current sub-circuit −Buffers at the boundary of a sub-circuit may also appear in another sub-circuit. ■Facilitating accurate reconstruction of circuit delay from sub-circuit simulation data ■Can reduce AC sweep time for sub-circuits 9TAU 2011, Myung-Chul Kim, University of Michigan
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Delay Propagation ■Purpose : After retrieving probing points’ delay from SPICE, they can be propagated in order to capture delay for probing points in subsequent sub-circuits. ■Calculation of delay from the root node s 0 to node s j −Find the sub-circuit containing s j. −Identify the shortest tree path from s 0 to s j, and the earliest node s i in the sub-circuit that lies on this tree path (Assume that signal delay from s 0 to s i was computed recursively). −The delay from s i to s j is obtained by SPICE simulation and added to delay at s i. 10TAU 2011, Myung-Chul Kim, University of Michigan
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Slew Propagation ■Purpose : After retrieving probing points’ slew from SPICE, they can be used in order to capture slew for probing points in subsequent sub-circuits. ■Slew at a given node can be expressed as a function of input slew of a sub-circuit. −Slew measured at the previous stage (up to the root node s i in a given sub-circuit) should be accounted for when stimuli for the current sub-circuit are generated. −Slew at a node is directly calculated by SPICE simulation. 11TAU 2011, Myung-Chul Kim, University of Michigan
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Empirical Results: ISPD10 Benchmarks ■Experimental setup −Single threaded runs on a 3.2GHz Intel core i7 Quad CPU Q660 Linux workstation −Buffered RC networks generated by applying Contango2 to ISPD’10 high-performance CNS contest benchmark suite −Open-source NgSPICE-2.2 ■Target granularity −Varies from (full-scale SPICE simulation) to in order to examine trade-offs 12TAU 2011, Myung-Chul Kim, University of Michigan
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Empirical Results: Avg. Error 13TAU 2011, Myung-Chul Kim, University of Michigan
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Empirical Results: Max. Error and Trade-off 14
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Fidelity ■Fidelity suggests whether Chop-SPICE is effective as a replacement of full-scale SPICE during optimization −On intermediate clock trees produced by Contango2, we use Chop-SPICE and full-scale SPICE to measure sink delays before and after optimization 15TAU 2011, Myung-Chul Kim, University of Michigan
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Future work ■Extension to general RC networks −An algorithm for computing signal delays in non-tree RC networks by partitioning a given circuit into a spanning tree and non-tree links, and invoking an RC-tree computation is given [6] −A recent study [16] report 98% correlation to full SPICE runs. ■Using parallelism −Two sub-circuits can be simulated in parallel if they do not lie on the same path to root. −The larger the RC tree, the more parallelism can be found. 16TAU 2011, Myung-Chul Kim, University of Michigan
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Conclusions ■Accurate estimation of circuit delay is becoming more difficult at new technology nodes −Clock-skew estimation in CNS requires picosecond precision ■Chop-SPICE partitions the original RC tree into sub-circuits, simulates each of them with SPICE, and reconstructs global results from simulation data for sub-circuits ■Empirical validation shows that Chop-SPICE offers attractive trade-offs between accuracy and runtime ■Chop-SPICE provides not only good accuracy, but also fidelity sufficient for use in external optimization algorithms ■Can be applied to any SPICE simulators 17TAU 2011, Myung-Chul Kim, University of Michigan
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Questions and Answers Thank you! Time for Questions 18TAU 2011, Myung-Chul Kim, University of Michigan
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