Design Space Exploration using Time and Resource Duality with the Ant Colony Optimization

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Design Space Exploration using Time and Resource
Duality with the Ant Colony Optimization

• Gang Wang, Wenrui Gong, Brian DeRenzi and Ryan
  Kastner
• Dept. of Electrical and Computer Engineering
• University of California, Santa Barbara
• DAC2006, San Francisco, California, July 24-28,
  2006

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Design Space Exploration

• DSE challenges to the designer
• Ever increasing design options
• Closely related w/ NP-hard problems
• Resource allocation
• scheduling
• Conflict objectives (speed, cost, power, )
• Increasing time-to-market pressure

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Our Focus Timing/Cost

• Timing/Cost Tradeoffs
• Known application
• Known resource types
• Known operation/resource mapping
• Question find the optimal timing/cost tradeoffs
• Most commonly faced problem
• Fundamental to other design considerations

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Common Strategies

• Usually done in a Ad-hoc way
• experience dependent
• Or Scanning the design space withResource
  Constrained (RCS) or Time Constrained (TCS)
  scheduling
• Whats the problem?
• RCS and TCS are Dual to Each Other

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Main Contributions

• New DSE algorithm leveraging duality
• New TCS/RCS algorithms using Ant Colony
  Optimization
• ExpressDFG a comprehensive benchmark

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Design Space Model
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Key Observations

• A feasible configuration C covers a beam starting
  from (tmin, C)
• tmin is the RCS result for C

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Design Space Model
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Key Observations

• A feasible configuration C covers a beam starting
  from (tmin, C)
• Optimal tradeoff curve L is monotonically
  non-increasing as deadline increases

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Design Space Model
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Theorem

• If C is the optimal TCS result at time t1, then
  the RCS result t2 of C satisfies t2 lt t1.
• More importantly, there is no configuration
  C'with a smaller cost can produce an execution
  time within t2, t1.

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Theorem (continued)
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What does it give us?

• It implies that we can construct L
• Starting from the rightmost t
• Find TCS solution C
• Push it to leftwards using RCS solution of C
• Do this iteratively (switch between TCS RCS)

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DSE Using Time/Resource Duality
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Solving TCS/RCS problems

• Exact method ILP
• Heuristic Methods
• Force-Directed Scheduling
• K-L Heuristic
• Genetic Algorithms
• Simulated Annealing

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Our approach Ant System Heuristic

• Inspired by ethological study on the behavior of
  ants Goss et. al. 1989
• A meta heuristic
• A multi-agent cooperative searching method
• A new way for combining global/local heuristics
• Extensible and flexible

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Ant System Heuristic
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Ant System Heuristic
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Ant System Heuristic
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Ant System Heuristic
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Ant System Heuristic
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Ant System Heuristic
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Ant System Heuristic
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Ant System Heuristic
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Ant System Heuristic
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ACO Based TCS/RCS

• Optimization ? Search
• Solution ? A chain of decisions
• Sub-decision ? global and local heuristics
• Iteratively construction and evaluation
• Heuristics is updated based on history
• Max-Min Ant System (MMAS)
• References Wang et al. 2005

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ExpressDFG

• A comprehensive benchmark for TCS/RCS
• Classic samples and more modern cases
• Comprehensive coverage
• Problem sizes
• Complexities
• Applications
• Downloadable from http//express.ece.ucsb.edu/benc
  hmark/

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Auto Regressive Filter
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Cosine Transform
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Matrix Inversion
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Experiments

• Three DSE approaches
• FDS Exhaustively scanning for TCS
• MMAS-TCS Exhaustively scanning for TCS
• MMAS-D Proposed method leveraging duality
• Scanning means that we perform TCS on each
  interested deadline

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Effectiveness of MMAS for TCS
MMAS-TCS
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DSE MMAS-D vs. FDS
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Experimental Results
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Timing Performance
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Conclusion

• Leverage duality between TCS/RCS for DSE
• ACO based TCS/RCS
• More stable/Better Performance
• Similar Computing Cost vs. FDS
• Thanks! Questions?