EECS 527 Paper Presentation

1. EECS 527 Paper Presentation Techniques for Fast Physical Synthesis By Charles J. Alpert, ShrirangK. Karandikar,Zhuo Li, Gi-Joon Nam, Stephen T. Quay,HaoxingRen, C. N. Sze, Paul G. Villarrubia,and Mehmet C. Yildiz Presented by LingfengXu Department Electrical Engineering and Computer Science University of Michigan, Ann Arbor 11/2011

2. EECS 527 Paper Presentation Outlines • Introduction • Buffering Trends • Major Phases of Physical Synthesis • Closer Look at Optimization • Selected Techniques • Fast Timing-Driven Buffering • Layout Aware Buffer Trees • Diffusion Based Legalization • Q&A

3. EECS 527 Paper Presentation Introduction • Purpose of physical synthesis • Timing closure • Physical synthesis • Iterations • Iterate between manual design work and automatic physical synthesis • Philosophy • As fast as possible even if a little optimality is sacrificed • IBM’s physical synthesis tool • PDS (Placement-Driven Synthesis) system

4. EECS 527 Paper Presentation Buffering trends • “Buffering Explosion” • Thiner wires == resistance increase • Wire delays increasingly dominate gate delays • Saxena et al. [3] predict that half of all logic will consist of buffers • 20% - 25% buffers or inverters in today’s 90nm design

5. Percentage of block-level nets requiring repeaters [3] • Intra-block communication repeaters as a percentage of the total cell count for the block [3]

6. EECS 527 Paper Presentation Buffering trends • Challenges • Buffer insertion need to be performed fast • Area and Power • Layout awareness • Buffering constricts or seeds global routing

7. EECS 527 Paper Presentation Major Phase of Physical Synthesis • PDS stages • Initial placement and optimization • Timing-driven placement and optimization • Timing-driven detailed placement • Optimization techniques • Clock insertion and optimization • Routing and post routing optimization • Early-mode timing optimization

8. EECS 527 Paper Presentation Closer look at Optimization • Optimization phases • Electrical correction • Critical path optimization • Histogram compression • Legalization • An example of physical synthesis breakdown

9. EECS 527 Paper Presentation How to Achieve Fast Physical Synthesis? • Selected Techniques • Fast Timing-Driven Buffering • Layout Aware Buffer Trees • Diffusion Based Legalization

10. EECS 527 Paper Presentation Fast Timing-Driven Buffering • Motivation • Over a million buffers • Rebuffering rips all buffers and reinserts buffers from scratch • Considerations • Buffering resources vs. delay • Runtime • Slew, noise and capacitance constraints

11. EECS 527 Paper Presentation Fast Timing-Driven Buffering • Classical Buffering Algorithm • Goal: Maximize source RAT • Dynamic programming • Candidate solutions generated and propagated from the sinks to the source • Solution internal node characteristics (q, c, w) • q: required arrival time • c: downstream load capacitance • w: cost summation for the buffer insertion decision • Example: sink (q = RAT, c = load capacitance, w = 0)

12. EECS 527 Paper Presentation Fast Timing-Driven Buffering • Classical Buffering Algorithm • Two solutions α1, α2 • α2dominates α1, if q2 ≥ q1, c2 ≤ c1and w2 ≤ w1 • α1is redundant and can be pruned • At the end of algorithm • A set of solutions with different cost-RAT tradeoff is obtained • Choose one in middle • “10 ps rule”: If margin RAT gain is more than 10ps,choose solution with bigger RAT

13. EECS 527 Paper Presentation Fast Timing-Driven Buffering • Prebuffer Slack Pruning (PSP) • Based on current node being processed • if q2 < q1, c2 < c1and (q2 - q1)/(c2 - c1) ≥ Rmin, then α2is pruned early • Appropriate Rmin guarantees optimality, however larger value does not hurt solution quality

14. EECS 527 Paper Presentation Fast Timing-Driven Buffering • Squeeze Pruning • Three partial solutions α1, α2, α3with same cost • if (q2 - q1)/(c2 - c1)≤(q3 - q2)/(c3 - c2), then α2is pruned • For a two-pin net, the middle point is always dominated by either the first or the third solution; for multi-sink net, optimality not guaranteed but causes no degradation in solution most of the time

15. EECS 527 Paper Presentation Fast Timing-Driven Buffering • Library Lookup • Every buffer in the library is examined for iterationIf there are m kinds of buffer and inverter, n nodes, mn candidate solutions in total • However many candidate solutions are not worth considering • Pre-compute Buffer table and Inverter table • 2n candidate solutions, n with inverters and n with buffers

16. EECS 527 Paper Presentation Fast Timing-Driven Buffering • Results and Summary • Derived from 5000 high capacitance nets from an ASIC chip • 3% quality degradation and 20x speedup • Philosophy: as fast as possible even if a little optimality is sacrificed • Rip up and rebuffering with more accurate techniques can be perform latter if desired

17. EECS 527 Paper Presentation Layout Aware Fast and Flexible Buffer Trees • Layout problems in buffering • (a) Alley • (b) Pile-ups • Holes in large blocks • Layout constrains • Holes in large blocks • Navigating blocks and denseregion • Critical and non-critical routes • Avoiding routing congestions

18. EECS 527 Paper Presentation Layout Aware Fast and Flexible Buffer Trees • Layout aware buffer tree flow • Step 1: Construct a fast timing-driven Steiner tree • Step 2: Reroute the Steiner tree to preserve its topology while navigating environmental constrains • Step 3: Insert buffers (e.g. with Fast Timing-Driven Buffering) • This work focuses on Step 2

19. EECS 527 Paper Presentation Layout Aware Fast and Flexible Buffer Trees • Algorithm • Break existing Steiner tree into disjoint 2-paths, i.e., paths start and end with either source, sink or a Steiner point • Each 2-path is routed in turn to minimize cost, starting from sinks and ending at source • Maze routing for each2-path with costfunction • If Steiner point is in acongested region,move it in a specified“plate region”

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22. EECS 527 Paper Presentation Layout Aware Fast and Flexible Buffer Trees • General Maze routing cost function • Tradeoff parameter 0 ≤ K ≤ 1 • Tile cost: cost(t) = 1 + K e(t) • Merging branches: cost(t) = max(cost(L), cost(R) + K min(cost(L), cost(R)) • Sink initializationcost(s) = (K - 1)RAT(s)/DpT • Use K=1 for electrical correction; use K=0.1 for critical path

23. EECS 527 Paper Presentation Layout Aware Fast and Flexible Buffer Trees • Example and Summary • A 7-pin net of an industrial design • (a) K=1.0, 4134ps slack improvement • (b) K=0.1, 4646ps slack improvement

24. EECS 527 Paper Presentation Diffusion-Based Placement Techniques for Legalization • Classical legalization • After optimization, local regions can be overfull • Run periodically to snap from overlaps to legal positions • If one waits too long between two legalizations, cells may end up quite far away from optimal position, which may severely hurt timing • Diffusion-Based Legalization • Avoid cells been moved too far away • Fast. Run in minutes on designs with millions of gates

25. EECS 527 Paper Presentation Diffusion-Based Placement Techniques for Legalization • Diffusion as a Physical Process • Moves elements from a state with non-zero potential energy to a state of equilibrium • Can be modeled by breaking down into finite time steps • Relationship of material concentration with time and space

26. EECS 527 Paper Presentation Diffusion-Based Placement Techniques for Legalization • Diffusion as a Physical Process • Cell velocity • Cell new location

27. EECS 527 Paper Presentation Diffusion-Based Placement Techniques for Legalization • Diffusion Based Placement • Coordinates are scaled so that the width and height of each bin is one • Location (x, y) lies in bin • Forward Time Centered Space (FTCS) schemeNew bin density • Bin velocity

28. EECS 527 Paper Presentation Diffusion-Based Placement Techniques for Legalization • Diffusion Based Placement • Enforce vH = 0 at horizontal boundary and vH = 0at vertical boundary • Two cells right next to each other can be assigned very different velocities which could change their relative ordering. Apply velocity interpolation based on the four closest bins to remedy this behavior • New locations (x, y) for the next time stamp

29. EECS 527 Paper Presentation Diffusion-Based Placement Techniques for Legalization • Diffusion Based Placement: Getting it work • Diffusion process reaches equilibrium when each bin has the same density, i.e. the average density, can cause unnecessary spreading, even if every bin’s density is well below dmax • Idea: Run diffusion for regions which requires it • Local Diffusion: Run diffusion on cells in a window around bins that violate target density constraint • If FTCS error exceeds a certain threshold, update the real density based on real cell placement and restart the diffusion algorithm

30. EECS 527 Paper Presentation Diffusion-Based Placement Techniques for Legalization • Example • Before legalization,after traditional legalizationand diffusion legalization • 4% total wire length save • 48% worst slack improvement • 36% less negative paths • Summary • Diffusion based legalizationis less likely to disrupt thestate of design

31. EECS 527 Paper Presentation Summary • Buffering trends • “Buffer Explosion” • Physical synthesis phases • 4 phases • Fast Timing-Driven Buffering • Layout Aware Buffer Trees • Diffusion-Based Legalization

32. EECS 527 Paper Presentation Thanks ! Q&A