1 / 14

Modeling and Sensitivity of Via Count in SOC Physical Implementation

This research delves into the modeling and sensitivity analysis of via count in System-on-Chip (SOC) physical implementation. It discusses the key parameters affecting via count, reviews existing via count estimation models, and proposes a new taxonomy for via count modeling approaches. The study explores the correlation between design parameters and via count, with a focus on routing congestion and track utilization.

gmathieu
Download Presentation

Modeling and Sensitivity of Via Count in SOC Physical Implementation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. On Modeling and Sensitivity of Via Count in SOC Physical ImplementationKwangok Jeong (kjeong@vlsicad.ucsd.edu) Andrew B. Kahng (abk@cs.ucsd.edu) Hailong Yao (hailong@cs.ucsd.edu) VLSI CAD LABORATORY UCSD Nov. 24, 2008

  2. Outline • Motivation • Review of Via Count Estimation • Key Parameters to Via Count • Taxonomy of Via Count Modeling Approaches • Verification of Model • Conclusion UCSD VLSI CAD Laboratory ISOCC-2008

  3. Motivation • Via analysis and estimation are of great importance • Yield: via open, high-resistance fault = key defect types • Evaluating performance of existing routers • 32nm via rules • Evaluating new technologies or designs • 3D implementation needs (via density, …) • Previous work based on “Rent’s parameter” • In this work, we give taxonomy of via modeling and propose a new via count model for placed designs average pins per gate Rent’s parameter number of terminals Landman et al., 1971 number of components UCSD VLSI CAD Laboratory ISOCC-2008

  4. Truncated binomial series in Davis’s model Review of Via Count Estimation • Uezono’s model (T. Uezono et al., ISQED, 2006.) • Heavily depends on wire length and track utilization l : estimated wirelength ut : track utilization • Up to 19% error in the experimental results • Questionable argument • Why does #via increase with decreasing Rent’s parameter? • Uezono’s model uses • Davis et al.’s wirelength distribution that has error in #net for length = 1 Correlation between the Rent’s parameter p and the number of vias (Uezono et al., ISQED06) UCSD VLSI CAD Laboratory ISOCC-2008

  5. Key Parameters Affecting Via Count • Experimental setup • For N, k, p, U, M and Tech: use Rentian circuit generator (gnl [1]) • For F: use two different clock frequency for AES from opencores.org • For k (in real design): AES SP&R with different cell sets (whole vs. restricted) [1] D. Stroobandt, P. Verplaetse and J. V. Campenhout, "Generating Synthetic Benchmark Circuits for Evaluating CAD Tools", IEEE Trans. On Computer-Aided Design Of Integrated Circuits and Systems, (19)(9) (2000), pp. 1011-1022. UCSD VLSI CAD Laboratory ISOCC-2008

  6. Sensitivity to Design Parameters(Rentian Circuits) • Observations • N   #vias  • k   #vias  • p   #vias  • U   #vias  • M   #vias  #vias vs. #instance (N), technology(Tech), #metal (M), and utilization (U) #vias vs. #pins per gate (k), and Rent’s parameter (p) Experimental results show #via increases even for small Rent’s parameters #points to be connected wirelength track utilization UCSD VLSI CAD Laboratory ISOCC-2008

  7. Sensitivity to Design Parameters (Real Designs) • AES (~15K) core from opencores.org • Clock frequency (F): 50MHz and 400MHz (8X) • #pins per gate (k) • Large-k: use a full set of cells in the library • Small-k: use only INV, BUF, NAND2 and DFF • Wiring pitch (wp): original, half (2x tracks)  #vias increases by 4~11% k increases from 2.7 to 3.6  #vias increases by 44%  #vias decreases by 17% #via vs. pins per gate (k) and frequency (F) #via vs. wiring pitch (wp) UCSD VLSI CAD Laboratory ISOCC-2008

  8. Summary of Sensitivities to Design Parameters Routing Congestion or Track Utilization UCSD VLSI CAD Laboratory ISOCC-2008

  9. Via Count Estimation • Key ideas • #vias strongly depends on kN = total #pins • M1 layer generally not used for cell connections  all pins need V12 • If no design rules, all routes can be implemented with 1-tier (H/V) of routing layers  No detours and no additional vias  baseline of #vias • Congestion increases #vias UCSD VLSI CAD Laboratory ISOCC-2008

  10. Model: • VL: baseline of #vias • Vcongestion: additional vias from routing congestion • VL calculation • H-BBOX model • #VIA12 = #pins ( ) • #VIA23 = #pins • V-BBOX • #VIA12 = #pins • #VIA23 = 2 x #pins • Vcongestion calculation Taxonomy of Via Modeling Approach (1) • Analytical method • Inputs: given N, k and p • Enable to estimate #vias prior to netlist • Netlist-based method • Inputs: extracted N, k and p • Parameters are extracted from netlist  better accuracy Aspect ratio of net’s bounding box < 1 (H-BBOX: 2kN) Aspect ratio of net’s bounding box > 1 (V-BBOX: 3kN) UCSD VLSI CAD Laboratory ISOCC-2008

  11. S: Sum of SMT length T: Sum of available track length Taxonomy of Via Modeling Approach (2) • Placement-based method • Inputs: placed design  accurate wirelength estimation • Baseline of #vias (VL) • Construct Steiner minimum tree (SMT) • Assign SMT segments to 1-tier routing layer • Horizontal  M3, Vertical segments  M2 • Diagonal segments  converted to ‘L’ shape  M2 and M3 • Count #vias at each point • Average routing congestion • Large congestion requires more vias • Placement-based via count model UCSD VLSI CAD Laboratory ISOCC-2008

  12. Model Fitting using Training Designs • Finding model coefficients • Initial guess: • : via count increase due to via blockages ~ 4 • : affects probability of via blockage  • Final model coefficients ( ) • Average 3.4% error Via blockage A via blockage can increase #vias by more than 4 UCSD VLSI CAD Laboratory ISOCC-2008

  13. Model Accuracy on Industry Designs • We apply proposed placement-based via count model to designs from different technologies and different routers • Our model shows 8% error in average UCSD VLSI CAD Laboratory ISOCC-2008

  14. Conclusion and Ongoing Work • Conclusion • We evaluate the sensitivities of design and technology parameters on via count • Propose a new accurate placement-based via count estimation model • Experimental results are promising • 3.4% error on training designs with various instance counts, pins per gate, number of metal layers and wiring pitches • 8% error on various industry designs from different technologies and routers • Ongoing work • Accurate wirelength estimation for analytical and netlist-based via count modeling • Feed into the development of improved routers • Ability to assess impact of varying design rules and/or different router runtime options UCSD VLSI CAD Laboratory ISOCC-2008

More Related