A genetic algorithm for the design space exploration of datapaths during high-level synthesis

Krishnan, Vyas; Katkoori, Srinivas

doi:10.1109/tevc.2005.860764

Cited by 108 publications

(158 citation statements)

References 34 publications

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“…Several multiobjective techniques have been proposed to solve engineering problems in the past decade and a half [7][8][9]. Multi-objective optimization of area and delay during datapath synthesis has been addressed in [2,3]. However power is not considered in these schemes.…”

Section: New Algorithmic Techniques Usedmentioning

confidence: 99%

“…Early work in Behavioral Synthesis focused on constructive approaches as in force directed scheduling [2,17]. These are essentially greedy approaches vulnerable to local minima.…”

Section: Review Of Previous Workmentioning

confidence: 99%

“…In [2], a weighted sum approach has been proposed for area and delay minimization during HLS of DFGs. In Ferrandi et al [3], the authors have used two different encodings which prioritize scheduling and binding, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…A true multi-objective GA (NSGA II) is used to optimize the schedules. We propose an evolutionary framework using the chromosome encoding in [2], with the cost function modified to incorporate power in addition to area and delay. Power cost of a schedule is determined as the likelihood of the schedule to yield low-power bindings, computed from compatibility graph metrics described in [4].…”

Section: Introductionmentioning

confidence: 99%

“…Power cost of a schedule is determined as the likelihood of the schedule to yield low-power bindings, computed from compatibility graph metrics described in [4]. The methodology is evaluated for WSGA and NSGA II on standard benchmarks [2,5] and the superiority of NSGA II is demonstrated. The framework is extended for applying WSPSO to the HLS scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

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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: New Algorithmic Techniques Usedmentioning

confidence: 99%

“…Early work in Behavioral Synthesis focused on constructive approaches as in force directed scheduling [2,17]. These are essentially greedy approaches vulnerable to local minima.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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A review of bioinspired computer‐aided design tools for hardware design

Lanchares¹,

Garnica²,

Fernández-de-Vega

et al. 2013

Concurrency and Computation

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During the last two decades, bioinspired techniques have emerged as a powerful optimization and problem solving tool for a wide range of real world applications. Parallel architectures and computer-aided design tools have been also influenced by this evolutionary fashion. In this paper, we give a broader view of the application of techniques inspired by nature to hardware design and parallel architectures problem solving. Our aim is to furnish an overview of the various bioinspired techniques based tools that have been used so far to solve the problems of automatic hardware design. We can claim that a lot of the approaches found in the literature suffer from a lack of interdisciplinary interaction among researchers of both evolutionary computation and hardware design fields. In addition, we have also detected that some multi-objective problems do not use the appropriate algorithms.Among widely known heuristics, gradient algorithms, such as Hill Climbing, were soon employed. These algorithms start the search process from a single point, stochastically generated from the solution space, and then perform a search process using the direction of the gradient towards the objective function. These algorithms are of an easy implementation and relatively efficient. However, local optima are usually the output and no general procedures exist to know the distance from the global optimum.An improvement over the previous approach was obtained by the simulated annealing (SA) algorithm [2], capable of removing some of the previously described disadvantages. SA begins with a randomly generated candidate solution-a point in the search space-with some specific features, and then, the algorithm proceeds by applying a disturbance to the candidate solution, thus trying to improve it. If the solution improves, it is selected and becomes the next candidate solution; otherwise, a probability decides whether to continue with the initial candidate or with the new one. The probability of accepting a worse solution depends on the system temperature-a fundamental parameter for SA. The system starts with a very high temperature, so most of the worse solutions are accepted at the beginning. The temperature is then lowered gradually; the lower the temperature, the lower the probability of accepting a worse solution. So far, SA has been the most frequently employed heuristic in computer-aided design (CAD) tools.Nevertheless, a new kind of heuristic has gained popularity: bioinspired algorithms (BAs) [3]. Among BAs, evolutionary algorithms (EAs) try to emulate the natural evolution of species, following Darwin's principles of natural selection. One of their main advantages is the employment of a set (population) of candidate solutions (individuals) at the same time, which gives rise to an intrinsically parallel search process. We can also find other bioinspired approaches on the basis of the social behavior of the species such as particle swarm optimization (PSO) [4] or ant colony optimization (ACO) [5] algorithms.This review focuses on the appli...

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Acceleration of the Discrete Element Method: From RTL to C-Based Design

Schafer

Wakabayashi

2013

High-Performance Computing Using FPGAs

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A genetic algorithm for the design space exploration of datapaths during high-level synthesis

Cited by 108 publications

References 34 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 review of bioinspired computer‐aided design tools for hardware design

Acceleration of the Discrete Element Method: From RTL to C-Based Design

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