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Architecture for Exploring Large Design Spaces

Architecture for Exploring Large Design Spaces. John R. Josephson, B. Chandrasekaran, Mark Carroll, Naresh Iyer, Bryon Wasacz, Qingyuan Li, Giorgio Rizzoni, David Erb. Architecture for exploring large design spaces. Three synergistic components. Seeker. Filter. Viewer. Design Seeker.

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Architecture for Exploring Large Design Spaces

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  1. ArchitectureforExploring Large Design Spaces John R. Josephson, B. Chandrasekaran, Mark Carroll, Naresh Iyer, Bryon Wasacz, Qingyuan Li, Giorgio Rizzoni, David Erb

  2. Architecture for exploring large design spaces Threesynergisticcomponents Seeker Filter Viewer

  3. Design Seeker • Human initiates automated design search which may work by considering combinations of: • generic devices (configurations) • alternative components • representative parameter values. • Designs are evaluated according to multiple criteria using simulation-based and other critics

  4. Design Seeker Device Library Critics Search control Constraints Evaluated designs

  5. Big search ! • Search may be massive and exhaustive. • Largest experiment to date • 2,152,698 designs were generated and evaluated, of which 1,796,025 were fully specified. • Each fully specified design was evaluated using multiple simulations. • Seeker used idle time on 209 workstations to search the space in 6.8 days (wall-clock time). (The maximum number running at any one time was 159.)

  6. Dominance Filter Dominance algorithm

  7. Dominance Filter • Design candidate A is said to dominate candidate B if A is superior or equal to B in every criterion of evaluation and strictly superior for at least one criterion. • Dominated designs are removed. (This is lossless) • Surviving designs are Pareto optimal (improvement on any criterion will reduce value on another) • Tolerances may be specified for the comparisons.

  8. Dominance Filter Dominance algorithm Dominance filtering can be very effective.

  9. Effectiveness of dominance filtering Using 4 criteria and reasonably realistic simulation models : Dominance filtering is very effective! Dominance filtering scales very well!

  10. Efficiency of dominance filtering algorithm 1,796,025 1,078 4.5 hours (serial post processing)

  11. Effect of number of criteria In experiment B with 17,711 designs: The effectiveness of dominance filtering apparently tends to decrease as the number of criteria increases.

  12. Interactive Viewer Filter Viewer Tradeoffs are explored interactively.

  13. Interactive Viewer • visualization of trade-offs • zooming to selected regions in trade-off space • selection of subsets by structural constraints (not implemented) • initiation of more focused search (not implemented) • initiation of additional search, e.g., add criteria (not implemented)

  14. Visualizing search results

  15. Visualizing search results

  16. Visualizing search results

  17. Visualizing search results

  18. Visualizing search results

  19. Visualizing search results

  20. Visualizing search results

  21. Visualizing search results

  22. Visualizing search results

  23. Exploring large design spaces Human-in-the-loop multi-criterial optimization Seeker Filter Viewer

  24. Patent application has been submitted.

  25. Next Steps • Technology for composable simulation models • Improved viewer - more types of displays • Automatic extraction of generalizations

  26. Questions?

  27. Design Seeker Essentially: • a generator of design • evaluators for designs

  28. More generally The Seeker consists of: • a generator of choice alternatives • evaluators for choice alternatives

  29. Clientstarter Clients Crit Server Crit Crit Crit Gen Out User Crit Crit Crit Filter In Crit Crit Crit Crit Crit Crit Crit Crit Crit Crit Crit Crit Crit Crit Seeker based on client-server

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