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 EngineeringUniversity of California, Santa Barbara

DAC’2006, San Francisco, California, July 24-28, 2006

Design Space Exploration

DSE challenges to the designerEver increasing design optionsClosely related w/ NP-hard problems

Resource allocationscheduling

Conflict objectives (speed, cost, power, …) Increasing time-to-market pressure

Our Focus: Timing/Cost

Timing/Cost TradeoffsKnown applicationKnown resource typesKnown operation/resource mapping

Question: find the optimal timing/cost tradeoffs

Most commonly faced problem Fundamental to other design considerations

Common Strategies

Usually done in a Ad-hoc way experience dependent

Or Scanning the design space withResource Constrained (RCS) or Time Constrained (TCS) scheduling

What’s the problem?RCS and TCS are Dual to Each Other

Main Contributions

New DSE algorithm leveraging duality New TCS/RCS algorithms using Ant Colony

Optimization ExpressDFG: a comprehensive benchmark

Design Space Model

Key Observations

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

Design Space Model

Key Observations

A feasible configuration C covers a beam starting from (tmin, C)

Optimal tradeoff curve L is monotonically non-increasing as deadline increases

Design Space Model

Theorem

If C is the optimal TCS result at time t1, then the RCS result t2 of C satisfies t2 <= t1.

More importantly, there is no configuration C′with a smaller cost can produce an execution time within [t2, t1].

Theorem (continued)

What does it give us?

It implies that we can construct L:Starting from the rightmost tFind TCS solution CPush it to leftwards using RCS solution of CDo this iteratively (switch between TCS + RCS)

DSE Using Time/Resource Duality

Solving TCS/RCS problems

Exact method: ILP Heuristic Methods

Force-Directed SchedulingK-L HeuristicGenetic AlgorithmsSimulated Annealing

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

Ant System Heuristic

Ant System Heuristic

Ant System Heuristic

Ant System Heuristic

Ant System Heuristic

Ant System Heuristic

Ant System Heuristic

Ant System Heuristic

Ant System Heuristic

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]

ExpressDFG

A comprehensive benchmark for TCS/RCSClassic samples and more modern casesComprehensive coverage

Problem sizesComplexitiesApplications

Downloadable from http://express.ece.ucsb.edu/benchmark/

Auto Regressive Filter

Cosine Transform

Matrix Inversion

Experiments

Three DSE approachesFDS: Exhaustively scanning for TCSMMAS-TCS: Exhaustively scanning for TCS MMAS-D: Proposed method leveraging duality

* Scanning means that we perform TCS on each interested deadline

Effectiveness of MMAS for TCS

MMAS-TCS

DSE: MMAS-D vs. FDS

Experimental Results

Timing Performance

Conclusion

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