design space exploration using time and resource duality with the ant colony optimization gang wang,...
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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 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
Key Observations
A feasible configuration C covers a beam starting from (tmin, C) tmin is the RCS result for C
Key Observations
A feasible configuration C covers a beam starting from (tmin, C)
Optimal tradeoff curve L is monotonically non-increasing as deadline increases
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].
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)
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
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/
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