Architecture for Exploring Large Design Spaces John R. Josephson, B. Chandrasekaran, Mark Carroll, Naresh Iyer, Bryon Wasacz, Qingyuan Li, Giorgio Rizzoni,

ArchitectureArchitecture forfor

Exploring Large Design SpacesExploring Large Design Spaces

John R. Josephson, B. Chandrasekaran,John R. Josephson, B. Chandrasekaran,Mark Carroll, Naresh Iyer,Mark Carroll, Naresh Iyer,

Bryon Wasacz, Qingyuan Li, Bryon Wasacz, Qingyuan Li, Giorgio Rizzoni, David ErbGiorgio Rizzoni, David Erb

Architecture for exploring large design Architecture for exploring large design spacesspaces

ThreeThree synergisticsynergistic componentscomponents

Seeker Filter Viewer

Design SeekerDesign 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


Device LibraryDevice Library CriticsCritics

Search controlSearch control

Evaluated designsEvaluated designsConstraintsConstraints

.

generic devicesdevicesFunctionCausalProcessStructure

achievemaintainpreventstatemodecomponentsnormalconnectionsspatial relationscomponents

Big search !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.)

Dominance FilterDominance Filter

Dominance algorithm

Dominance FilterDominance 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.

Dominance FilterDominance Filter

Dominance algorithm

Dominance filtering can be very effective.

Effectiveness of dominance Effectiveness of dominance filteringfiltering

Using 4 criteria and reasonably realistic simulation models :

Dominance filtering is very effective!Dominance filtering scales very well!

Experiment No. designs considered Survivors % Survivors

A 1,798 71 3.95%B 17,711 173 0.98%C 179,874 556 0.31%D 1,796,025 1,078 0.06%

Efficiency of dominance filtering Efficiency of dominance filtering algorithmalgorithm

1,796,025 1,078

4.5 hours (serial post processing)

Effect of number of criteriaEffect of number of criteriaIn experiment B with 17,711 designs:

The effectiveness of dominance filtering apparently tends to decrease as the number of criteria increases.

No. criteria No. survivors2 18 (avg., ±18)3 74.25 (avg., ± 39.25)4 143

Interactive ViewerInteractive Viewer

Tradeoffs are explored interactively.

Filter Viewer

Interactive ViewerInteractive 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)

Visualizing search resultsVisualizing search results









Exploring large design spacesExploring large design spaces

Human-in-the-loop multi-criterial optimization

Seeker Filter Viewer

Patent application has been Patent application has been submitted.submitted.

Next StepsNext Steps

• Technology for composable simulation Technology for composable simulation modelsmodels

• Improved viewer - more types of displaysImproved viewer - more types of displays

• Automatic extraction of generalizationsAutomatic extraction of generalizations

Questions?


Essentially:Essentially:

• a generator of designa generator of design

• evaluators for designsevaluators for designs

More generallyMore generally

The Seeker consists of:The Seeker consists of:

• a generator of choice alternativesa generator of choice alternatives

• evaluators for choice alternativesevaluators for choice alternatives

Seeker based on client-server Seeker based on client-server

User

Server

FilterFilter

GenGen

InIn

OutOut

Clientstarter Clients

CritCritCritCrit

CritCrit

CritCrit

CritCritCritCrit

CritCrit

CritCrit

CritCritCritCrit

CritCrit

CritCrit

CritCritCritCrit

CritCritCritCrit

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Documents

Architecture for Exploring Large Design Spaces John R. Josephson, B. Chandrasekaran, Mark Carroll, Naresh Iyer, Bryon Wasacz, Qingyuan Li, Giorgio Rizzoni,