A game theoretic approach for power optimization during behavioral synthesis

Murugavel, Ashok K.; Ranganathan, N.

doi:10.1109/tvlsi.2003.819566

Cited by 22 publications

(3 citation statements)

References 24 publications

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“…An ILP approach is combined with retiming for power optimization in [21]. In [22], a game-theoretic approach for power optimization of a scheduled DFG is proposed. The functional units are modeled as bidders for the operations in the DFG with power consumption as the cost.…”

Section: Review Of Previous Workmentioning

confidence: 99%

A Novel Framework for Applying Multiobjective GA and PSO Based Approaches for Simultaneous Area, Delay, and Power Optimization in High Level Synthesis of Datapaths

2012

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High-Level Synthesis deals with the translation of algorithmic descriptions into an RTL implementation. It is highly multiobjective in nature, necessitating trade-offs between mutually conflicting objectives such as area, power and delay. Thus design space exploration is integral to the High Level Synthesis process for early assessment of the impact of these trade-offs. We propose a methodology for multi-objective optimization of Area, Power and Delay during High Level Synthesis of data paths from Data Flow Graphs (DFGs). The technique performs scheduling and allocation of functional units and registers concurrently. A novel metric based technique is incorporated into the algorithm to estimate the likelihood of a schedule to yield low-power solutions. A true multi-objective evolutionary technique, "Nondominated Sorting Genetic Algorithm II" (NSGA II) is used in this work. Results on standard DFG benchmarks indicate that the NSGA II based approach is much faster than a weighted sum GA approach. It also yields superior solutions in terms of diversity and closeness to the true Pareto front. In addition a framework for applying another evolutionary technique: Weighted Sum Particle Swarm Optimization (WSPSO) is also reported. It is observed that compared to WSGA, WSPSO shows considerable improvement in execution time with comparable solution quality.

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Section: Review Of Previous Workmentioning

confidence: 99%

A Novel Framework for Applying Multiobjective GA and PSO Based Approaches for Simultaneous Area, Delay, and Power Optimization in High Level Synthesis of Datapaths

2012

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“…Microeconomic approaches have been applied to a wide spectrum of problems in the domain of computer science. Murugavel and Ranganathan [30] developed auction theoretic algorithms in VLSI design automation for simultaneous gate sizing and buffer insertion problem. In [17], Hanchate and Ranganathan have applied game theoretic concepts for simultaneous optimization of interconnect delay and crosstalk noise through gate sizing, and in [16], Gupta and Ranganathan have implemented game theory for resource allocation and scheduling in the field of multiemergency management.…”

Section: Related Workmentioning

confidence: 99%

A Game Theoretic Approach for Simultaneous Compaction and Equipartitioning of Spatial Data Sets

Gupta

Ranganathan

2010

IEEE Trans. Knowl. Data Eng.

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Abstract-Data and object clustering techniques are used in a wide variety of scientific applications such as biology, pattern recognition, information systems, etc. Traditionally, clustering methods have focused on optimizing a single metric, however, several multidisciplinary applications such as robot team deployment, ad hoc networks, facility location, etc., require the simultaneous examination of multiple metrics during clustering. In this paper, we propose a novel approach for spatial data clustering based on the concepts of microeconomic theory, which can simultaneously optimize both the compaction and the equipartitioning objectives. The algorithm models a multistep, normal form game consisting of randomly initialized clusters as players that compete for the allocation of data objects from resource locations. A Nash-equilibrium-based methodology is used to derive solutions that are socially fair for all the players. After each step, the clusters are updated using the KMeans algorithm, and the process is repeated until the stopping criteria are satisfied. Extensive simulations were performed on several real data sets as well as artificially synthesized data sets to evaluate the efficacy of the algorithm. Experimental results indicate that the proposed algorithm yields significantly better results as compared to the traditional algorithms. Further, the proposed algorithm yields a high value of fairness, a metric that indicates the quality of the solution in terms of simultaneous optimization of the objectives. Also, the sensitivity of the various design parameters on the performance of our algorithm is analyzed and reported.

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“…A preliminary study on the optimization of interconnect delay and crosstalk • N. Hanchate and N. Ranganathan noise during wire sizing can be found in Hanchate and Ranganathan [2006]. Game theory has been successfully used to solve several VLSI domain problems [Shukla and Gupta 2001;Ranganathan and Murugavel 2003;Murugavel and Ranganathan 2003]. The motivation for using game theory to solve the wire sizing problem is discussed presently.…”

Section: Related Workmentioning

confidence: 99%

A game-theoretic framework for multimetric optimization of interconnect delay, power, and crosstalk noise during wire sizing

Hanchate

Ranganathan

2004

ACM Trans. Des. Autom. Electron. Syst.

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The continuous scaling of interconnect wires in deep submicron (DSM) circuits results in increased interconnect delay, power, and crosstalk noise. In this work, we develop a game-theoretic framework and multimetric optimization algorithms for the simultaneous optimization during wire sizing of (i) interconnect delay and crosstalk noise, and (ii) interconnect delay, power, and crosstalk noise. We formulate the wire sizing optimization problem as a normal form-game model and solve it using Nash equilibrium theory. Game theory allows the optimization of multiple metrics with conflicting objectives. This property is exploited in modeling the wire sizing problem while simultaneously optimizing various design parameters like interconnect delay, power, and crosstalk noise, which are conflicting in nature. The nets connecting the driving cell and the driven cell are divided into net segments. The net segments within a channel are modeled as players and the range of possible wire sizes forms the set of strategies. The payoff function is modeled (i) as the geometric mean of interconnect delay and crosstalk noise in the case of first formulation, and (ii) as the weighted sum of interconnect delay, power, and crosstalk noise in the second formulation. The net segments are optimized from the ones closest to the driven cell towards the ones at the driving cell. Complete information about the coupling effects among the nets is extracted after the detailed routing phase. The time and space complexities of the proposed wire sizing formulations are linear in terms of the number of net segments. Experimental results on several medium and large opencore designs indicate that the proposed algorithm for simultaneous optimization of interconnect delay and crosstalk noise yields an average reduction of 21.48% in interconnect delay and a 26.25% reduction in crosstalk noise without any area overhead, over and above the optimization from the Cadence place and route tools. It is shown through experimental results that the algorithm performs significantly better than simulated annealing and genetic search. Further, new simple but accurate models are developed for three parallel interconnect net segments. It is shown that these models yield the same level of accuracy with significantly better run times compared to the models reported in Chen et al. [2004]. A mathematical proof of existence for the Nash equilibrium solution for the proposed wire sizing formulation is also provided.

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A game theoretic approach for power optimization during behavioral synthesis

Cited by 22 publications

References 24 publications

A Novel Framework for Applying Multiobjective GA and PSO Based Approaches for Simultaneous Area, Delay, and Power Optimization in High Level Synthesis of Datapaths

A Novel Framework for Applying Multiobjective GA and PSO Based Approaches for Simultaneous Area, Delay, and Power Optimization in High Level Synthesis of Datapaths

A Game Theoretic Approach for Simultaneous Compaction and Equipartitioning of Spatial Data Sets

A game-theoretic framework for multimetric optimization of interconnect delay, power, and crosstalk noise during wire sizing

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