1. Task 1091.001: Highly Scalable Placement by Multilevel Optimization Task Leaders: Jason Cong (UCLA CS) and Tony Chan (UCLA Math) Students with Graduation Dates: Michalis Romesis (UCLA CS, March 2005 ---graduated) Kenton Sze (UCLA Math, July 2006 --- graduated) Min Xie (UCLA CS, September 2006 --- graduated) Guojie Luo (UCLA CS, September 2010) Research Staff: Joe Shinnerl, UCLA CS

2. 2015-6-23UCLA VLSICAD LAB2 Industrial Liaisons u Patrick McGuinness, Freescale Semiconductor, Inc. u Natesan Venkateswaran, IBM Corporation u Amit Chowdhary, Intel Corporation

3. 2015-6-23UCLA VLSICAD LAB3 Task Description and Anticipated Result u Highly scalable multilevel, multiheuristic placement algorithms that address the critical placement needs of nanometer designs:  scalability  multi-constraint optimization --- timing, routability, power, manufacturability, etc.  support of mixed-sized placement and incremental design. u Quantitative study of the optimality and scalability of placement algorithms  Construction of synthetic benchmarks with known optima to identify the deficiencies of existing methods u Our goal is to achieve one-process-generation benefit through innovation of physical-design technologies, especially placement.

4. 2015-6-23UCLA VLSICAD LAB4 Task Deliverables u Report on new placement benchmarks with known optimal or near optimal solutions for all major objectives and constraints. Scalability and optimization studies on existing placement techniques (Completed 3-Nov-2003) u Experiments and reports on the applicability of integrated AMG-based weighted aggregation and weighted interpolation. Improvement measured on both PEKO examples and industrial examples from SRC member companies (Completed 1- Jun-2004) u Experiments and reports on multiheuristic, multilevel relaxation and the scalable incorporation of complex constraints into the enhanced multilevel framework. Improvement measured on both PEKO and industrial examples (Completed 1- Jun-2005) u A highly scalable placement tool that (i) supports multi-constraint optimization, mixed-sized placement, and incremental design and (ii) produces best-of-class results for both PEKO and industrial examples from SRC member companies (Completed 1-Jun-2006) u Final report summarizing research accomplishments and future direction (Planned-Oct-31, 2006)

5. 2015-6-23UCLA VLSICAD LAB5 Accomplishments in the Past Year 1. Improvements in mPL for routing density control [Best quality, ISPD 2006 contest] 2. Thermal-Driven Placement 3. Heterogeneous Placement

6. 2015-6-23UCLA VLSICAD LAB6 Relative Wirelength year 20002001 20022003 2004 UNIFORM CELL SIZE NON-UNIFORM CELL SIZE A Brief History of mPL 2005 2006 mPL 5.0 Multilevel force directed Mixed-size capability mPL 6.0 Enhanced Routability handling mPL 1.0 [ICCAD00] ESC Clustering Goto relaxation mPL 1.1 FC clustering Partitioning added to legalization mPL 2.0 RDFL relaxation Primal-dual netlist pruning mPL 3.0 [ICCAD03] QRS relaxation AMG interpolation Multiple V cycles mPL 4.0 Improved DP Backtracking V cycle

7. 2015-6-23UCLA VLSICAD LAB7 mPL: Generalized Force-Directed Placement u Use of accurate objective functions [Bertsekas, 82, Naylor et al, 01] u Optimization-based bin-density constraint formulation u Iterative Uzawa solver u Multilevel for better runtime and wirelength is a generalized force

8. 2015-6-23UCLA VLSICAD LAB8 Accomplishments in the Past Year 1. Improvements in mPL for routing density control [Best quality, ISPD 2006 contest] 2. Thermal-Driven Placement 3. Heterogeneous Placement

9. 2015-6-23UCLA VLSICAD LAB9 Core Engine for Density Control u Overall scheme  One V cycle with comparable quality  Minimum perturbation in the last stages of GFD  Significant speed up without losing solution quality u Routing density handling  Residual density in each bin  Even distribution of dummy density into bins  Cell area inflation for better convergence Initial Finest Problem Final Placement coarsening interpolation Coarsest Problem GFD with Density Control Minimun perturbation

10. 2015-6-23UCLA VLSICAD LAB10 Macro Spreading u Need area density below target value [Nam, ISPD06] u Target distance between neighboring macros   : target density u Spreading represented as objective W H w w1w1 w2w2 A1A1 A2A2 f ij x H ij  dx i and dy i : perturbation  fx ij and fy ij : piece-wise linear function

11. 2015-6-23UCLA VLSICAD LAB11 Experiment Results on ISPD06 mPL6 produces the best solution quality using ISPD06 routability-driven metric

12. Demonstration of mPL6 http://cadlab.cs.ucla.edu/cpmo/videos/mPL6-density.wmv 2015-6-23UCLA VLSICAD LAB12

13. 2015-6-23UCLA VLSICAD LAB13 Accomplishments in the Past Year 1. Improvements in mPL core engine for mixed-size global placement 2. Thermal-Driven Placement 3. Heterogeneous Placement

14. 2015-6-23UCLA VLSICAD LAB14 Motivation u High power density due to technology scaling u Problems caused by high temperature  Hot spots become more harmful Higher temperature  Higher leakage power  More heat Higher temperature  Higher leakage power  More heat  Previously negligible effects become first-order effects Difficult estimation for power, timing, etc Difficult estimation for power, timing, etc

15. 2015-6-23UCLA VLSICAD LAB15 Thermal Model u One layer mesh to model the substrate  Σ j (T i - T j ) C xy + (T i – T sink ) C z = P i C xy, C z are the thermal conductance for the substrate and the heat sink C xy, C z are the thermal conductance for the substrate and the heat sink  Solved by Fast DCT Solve T from CT = P, given C and P Solve T from CT = P, given C and P Diagonalize C = Γ T ΛΓ Diagonalize C = Γ T ΛΓ u Γ is the discrete cosine matrix u Λ is a diagonal matrix T = Γ -1 Λ -1 Γ P T = Γ -1 Λ -1 Γ P TiTi T j,1 T j,2 T j,3 T j,4 T sink P C xy CzCz

16. 2015-6-23UCLA VLSICAD LAB16 Formulation & Solution u  Implement  i (x) and t i (x) with filler cells and “filler power” without area  T des is a given by user u Solved by Uzawa Algorithm u As additional thermal-aware GFD following a WL-driven V-Cycle

17. 2015-6-23UCLA VLSICAD LAB17 Experiment Results on IBM-FastPlace u Quality improvement  T even is the ideal temperature with the same total power  Max. on-chip temperature: T init after Step 1 T init after Step 1 T final = T des after Step T final = T des after Step u More than 90% quality improvement within 5% WL increase

18. 2015-6-23UCLA VLSICAD LAB18 Accomplishments in the Past Year 1. Improvements in mPL for routing density control [1 st quality, ISPD 2006 contest] 2. Thermal-Driven Placement 3. Heterogeneous Placement

19. 2015-6-23UCLA VLSICAD LAB19 Motivation u Need for placement on array type chips with pre-fabricated resources  FPGA  Structured ASIC u Need for heterogeneous capability  Memory, DSP, etc  Block on sites of the same type

20. 2015-6-23UCLA VLSICAD LAB20 Related Work u Academia  VPR [Betz & Rose 97], PATH [Kong 02], SPCD [Chen & Cong 04,05], PPFF [Maidee et al, 03], CAPRI []  VPR [Betz & Rose 97], PATH [Kong 02], SPCD [Chen & Cong 04,05], PPFF [Maidee et al, 03], CAPRI [Gopalakrishnan et al, 06]  Most comparisons to out-dated tools  No heterogeneous capability u Industry  Quartus II [Altera Corp.], ISE [Xilinx Inc.]  Proprietary chips only  Techniques not publicly documented

21. 2015-6-23UCLA VLSICAD LAB21 Heterogeneous Placement by mPL-H u First analytical placer for heterogeneous placement u Framework based on mPL6 [Chan et al, 05] u Multiple layered placement  One logical layer for each resource  Forbidden regions blocked by obstacles  Uniform wirelength computation u Filler cells on each layer DSP M-RAM LAB

22. Demonstration of mPL-H http://cadlab.cs.ucla.edu/cpmo/videos/mPL-H.wmv 2015-6-23UCLA VLSICAD LAB22

23. 2015-6-23UCLA VLSICAD LAB23 Experiment Setting Quartus_map Verilog netlist Quartus_fittermPL-H Clustered.vqm netlist Quartus_router Chip type Stratix Description.xml.qsf placement

24. 2015-6-23UCLA VLSICAD LAB24 Wirelength Comparison u WL still important for architecture evaluation u mPL-H is 3% better in HPWL, and 2% better in routed WL than Quartus II v5.0

25. 2015-6-23UCLA VLSICAD LAB25 Runtime Comparison u mPL-H can be 2X faster than Quartus II v5.0 when the circuit becomes sufficiently large

26. 2015-6-23UCLA VLSICAD LAB26 Overall Accomplishments Over the Funding Period u 34% reduction in WL over 3 years u One technology generation advancement

27. 2015-6-23UCLA VLSICAD LAB27 Technology Transfer in 2006 u Discussions at conferences and workshops  ASPDAC 2006, Yokohama, Japan  ISPD 2006, San Jose, USA  DAC 2006, San Francisco, USA u Benchmark Releases (PEKO-MS) http://cadlab.cs.ucla.edu/~pubbench http://cadlab.cs.ucla.edu/~pubbench u mPL release: http://cadlab.cs.ucla.edu/src_686_mpl/ http://cadlab.cs.ucla.edu/src_686_mpl/

28. 2015-6-23UCLA VLSICAD LAB28 Software Download Record u PEKO/PEKU [2002 – now]  More than 360 downloads… SRC member companies SRC member companies u Cadence, IBM, Intel, Mentor Graphics,…etc. NON-SRC member companies NON-SRC member companies u Synopsys, Magma, Monterey Design, etc. Universities Universities u CMU, Michigan, MIT, UC Berkeley, UCSD, …etc., u mPL [2001 – now]  More than 480 downloads… SRC member companies SRC member companies u Cadence, Intel, Mentor Graphics,…etc. NON-SRC member companies NON-SRC member companies u Synopsys, Magma, Intrinsity, Oasys, etc. Universities Universities u CMU, Michigan, Stanford, UCSD, Nat’l Taiwan U., …etc.,

29. 2015-6-23UCLA VLSICAD LAB29 Publications in 2006 u Conference papers  ASPDAC 2006: J. Cong, M. Xie, “ A Robust Detailed Placement for Mixed-size IC Designs.”  ISPD 2006: T. F. Chan, J. Cong, J. Shinnerl, K. Sze and M. Xie, “ mPL6: Enhanced Multilevel Mixed-size Placement.” u Thesis  Kenton Sze, “ Multilevel Optimization for VLSI Circuit Placement. ”  Min Xie, “Constraint-Driven Large Scale Circuit Placement Algorithms.”

30. 2015-6-23UCLA VLSICAD LAB30 Room for Further Improvement? u “Swirls” are difficult to correct with localized refinement mPL4 mPL5