Robert Lembach Rafael A. Arce-Nazario Donald Eisenmenger Cory Wood

1. A Diagnostic Method for Detectingand Assessing the Impact of Physical Design Optimizations on Routing Robert Lembach Rafael A. Arce-Nazario Donald Eisenmenger Cory Wood IBM Engineering and Technology Services 2005 International Symposium on Physical Design

2. Agenda • Appreciation • Motivation and Goals • Process Flow • Examples • Summary 2005 International Symposium on Physical Design

3. Motivation – Improve Physical Design Quality • Serendipitous observations by physical designers using a variety of physical optimizations systems • Poorly placed objects • Sub-optimal buffer topologies or placements • White space distribution issues • Complexity: algorithms, versions, parameters, interactions • Routing is being negatively impacted 2005 International Symposium on Physical Design

4. What a Long Strange Trip It’s Been 2005 International Symposium on Physical Design

5. Goals • Enable an independent audit of physical designs from a variety of physical design systems • Be exhaustive in scope • Initial focus on rapidly increasing buffer quantities • Easy to understand algorithm and metrics • Enable data mining 2005 International Symposium on Physical Design

6. Process Flow • Interrogate net list to extract disjoint groups • Execute algorithm on each group • Data mining 2005 International Symposium on Physical Design

7. Group Creation • Groups can be serial and/or parallel buffering trees or other logic boxes. • Groups are disjoint • File is input for • algorithm • graphic tools • data mining 2005 International Symposium on Physical Design

8. Group Statistics for 8 Chip Designs • Chart includes only the transparent buffering cells • Non-buffer groups also suitable for analysis 2005 International Symposium on Physical Design

9. Group Colorization • From BlueGene/L chip (ISSCC 2005) • Example of group use • In a routing hotspot, find and move arbitrarily placed buffering to free up routing channels 2005 International Symposium on Physical Design

10. OOB (Out of Bounds) Algorithm • Compares original network to reduced network with buffering made transparent • Calculated for each group • Quality metrics • Bloat length, ratio, density • Laps around the chip 2005 International Symposium on Physical Design

11. Data Mining: Meandering Buffer Chain • Data mining technique • Review 2-pin networks (buffering is transparent) • OOB identifies this layout as grossly out of bounds with high bloat length and bloat ratio • This area was hard to route 2005 International Symposium on Physical Design

12. Data Mining: Tuning Fork Topology • Physical synthesis adds buffer near source to drive one of two far sinks. Far sinks are near each other. • OOB predicts ~2x bloat, a doubling of routing demand • Routing may be degraded if transform is repeated many times in local area 2005 International Symposium on Physical Design

13. Data Mining: Tuning Fork Topology • Meandering nets reflect locally difficult routing • OOB using actual routes shows >2x bloat length • One of several similar transforms in this area • Timing surprises • OOB can use estimated or actual routes 2005 International Symposium on Physical Design

14. Data Mining: Non-buffer Groups • OOB can be extended beyond buffered networks • Example: 4-way OR with fanout of 1 on each net • OOB predicts ~3X bloat length for this configuration • For routing, better to fracture high function library elements, especially if they are locally clustered 2005 International Symposium on Physical Design

15. Data Mining: One Bit in a Bus • OOB detects high bloat length and ratio in simple buffer chain which one bit of a a larger bus • Physical synthesis attempts to use holes punched in large object 2005 International Symposium on Physical Design

16. Data Mining: All Bits • OOB detects issues wide variation in solution quality • Physical synthesis attempts to randomly distribute the buffering • Placement of buffering impacts routing, even if bloat is minimal 2005 International Symposium on Physical Design

17. Data Mining: Placement Anomalies 2005 International Symposium on Physical Design

18. Data Mining: One-box OOB Groups • Full chip view of bloat • Objects can be displaced during legalization or overlap removal • Addition of buffering is usually very non-uniform • Useful in floor plan closure 2005 International Symposium on Physical Design

19. Data Mining: Creative Buffering Schemes • White object drives blue buffer and yellow objects • Blue buffer drives red objects • Blue buffer added to reduce load on white object • Nearly doubles local wiring demand due to two nearly identical nets • OOB: ~1.8x bloat ratio 2005 International Symposium on Physical Design

20. Data Mining: Artificially Induced Problems 2005 International Symposium on Physical Design

21. Up to 10% of Chip Wire May Be Unnecessary • Buffer bloat (4%) • Poor topology or poor placement of buffering • Collateral Damage (4%) • Proximate nets meandering due to added routing stress • Proximate objects perturbed by buffer insertion • Non-buffer bloat (2%) • Library selection and influence on routing 2005 International Symposium on Physical Design

22. Summary • PD observations drove review of current practices • Current tools do significant routing damage, with up to 10% of total chip wire unnecessary • OOB flow is one way to track solution quality • Data mining used to identify problems and trends 2005 International Symposium on Physical Design