Multiobjective optimization and evolutionary algorithms for the application mapping problem in multiprocessor system-on-chip design

Erbas, Cagkan; Cerav-Erbas, Selin; Pimentel, Andy D.

doi:10.1109/tevc.2005.860766

Cited by 149 publications

(112 citation statements)

References 40 publications

(54 reference statements)

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“…Most of the approaches in the embedded systems domain are targeted to the system-level exploration of heterogeneous MP-SoCs [3,4,9,10,11]. Although these efforts are fairly efficient to explore, e.g., the various alternatives for mapping a specific application onto a target MP-SoC architecture, they typically still require significant efforts to (re-)write the scripts that control the evaluation mechanism (analytical model or simulator) during the search through the design space.…”

Section: Related Workmentioning

confidence: 99%

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NASA: A generic infrastructure for system-level MP-SoC design space exploration

Jia

Pimentel

Thompson

et al. 2010

2010 8th IEEE Workshop on Embedded Systems for Real-Time Multimedia

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Abstract-System-level simulation and design space exploration (DSE) are key ingredients for the design of multiprocessor system-on-chip (MP-SoC) based embedded systems. The efforts in this area, however, typically use ad-hoc software infrastructures to facilitate and support the system-level DSE experiments. In this paper, we present a new, generic systemlevel MP-SoC DSE infrastructure, called NASA (Non Ad-hoc Search Algorithm). This highly modular framework uses welldefined interfaces to easily integrate different system-level simulation tools as well as different combinations of search strategies in a simple plug-and-play fashion. Moreover, NASA deploys a so-called dimension-oriented DSE approach, allowing designers to configure the appropriate number of, possibly different, search algorithms to simultaneously co-explore the various design space dimensions. As a result, NASA provides a flexible and re-usable framework for the systematic exploration of the multi-dimensional MP-SoC design space, starting from a set of relatively simple user specifications. To demonstrate the distinct aspects of NASA, we also present several DSE experiments in which we, e.g., compare NASA configurations using a single search algorithm for all design space dimensions to configurations using a separate search algorithm per dimension. These experiments indicate that the latter multi-dimensional coexploration can find better design points and evaluates a higher diversity of design alternatives as compared to the more traditional approach of using a single search algorithm for all dimensions.

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Section: Related Workmentioning

confidence: 99%

“…In this paper, we focus on a Search module implementation based on genetic algorithms (GA). GA-based DSE has been widely studied in the domain of system-level design [6,9,17,18], and it has been demonstrated to yield good results.…”

Section: B Search Modulementioning

confidence: 99%

NASA: A generic infrastructure for system-level MP-SoC design space exploration

Jia

Pimentel

Thompson

et al. 2010

2010 8th IEEE Workshop on Embedded Systems for Real-Time Multimedia

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“…Using a multi-objective evolutionary optimizer [3], we intend to find a set of optimal design points (in terms of alternative architectural solutions and mappings) under three criteria: processing time, energy consumption and architecture cost.…”

Section: A Case Studymentioning

confidence: 99%

“…In [3], e.g., the mapping decision problem is formulated as a multi-objective optimization problem in which three criteria are considered: the processing time, energy consumption and cost of the architecture. To solve this problem, an Evolutionary Algorithm (EA) has been used to achieve a set of best alternative mapping decisions under the aforementioned multiple criteria.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1. Example of raw data generated by an EA Therefore, we have developed a novel interactive visualization tool, VMODEX 1 , to understand how an evolutionary algorithm, such as presented in [3], searches the design space, where the optimum design points are located, how design parameters influence each objective, and provides insight into the relationship between the different objectives. The main challenge that needs to be addressed by such a visualization environment is how the raw data (as illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Visualization of Multi-objective Design Space Exploration for Embedded Systems

Taghavi

Pimentel

2010

2010 13th Euromicro Conference on Digital System Design: Architectures, Methods and Tools

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Abstract-Modern embedded systems come with contradictory design constraints. On one hand, these systems often target mass production and battery-based devices, and therefore should be cheap and power efficient. On the other hand, they need to achieve high (real-time) performance. This wide spectrum of design requirements leads to complex heterogeneous system-onchip (SoC) architectures. The complexity of embedded systems forces designers to model and simulate systems and their components to explore the wide range of design choices. Such design space exploration is especially needed during the early design stages, where the design space is at its largest.Due to the exponential design space in real problems and multiple criteria to be considered, multi-objective evolutionary algorithms (MOEAs) are often used to trim down a large design space into a finite set of points and provide the designer a set of tradable solutions with respect to the design criteria.Interpreting the search results (e.g., where are the Pareto points located), understanding their relations and analyzing how the design space was searched by such searching algorithms is of invaluable importance to the designer. To this end, this paper presents a novel interactive visualization tool, based on tree visualization, to understand the search dynamics of a MOEA and to visualize where the optimum design points are located in the design space and what objective values they have.

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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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Multiobjective optimization and evolutionary algorithms for the application mapping problem in multiprocessor system-on-chip design

Cited by 149 publications

References 40 publications

NASA: A generic infrastructure for system-level MP-SoC design space exploration

NASA: A generic infrastructure for system-level MP-SoC design space exploration

Visualization of Multi-objective Design Space Exploration for Embedded Systems

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

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