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Scenario-Based Design Space Exploration

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Handbook of Hardware/Software Codesign

Abstract

Modern embedded systems are becoming increasingly multifunctional, and, as a consequence, they more and more have to deal with dynamic application workloads. This dynamism manifests itself in the presence of multiple applications that can simultaneously execute and contend for resources in a single embedded system as well as the dynamic behavior within applications themselves. Such dynamic behavior in application workloads must be taken into account during the early system-level Design Space Exploration (DSE) of Multiprocessor System-on-Chip (MPSoC)-based embedded systems. Scenario-based DSE utilizes the concept of application scenarios to search for optimal mappings of a multi-application workload onto an MPSoC. To this end, scenario-based DSE uses a multi-objective genetic algorithm (GA) to identify the mapping with the best average quality for all the application scenarios in the workload. In order to keep the exploration of the scenario-based DSE efficient, fitness prediction is used to obtain the quality of a mapping. This fitness prediction implies that instead of using the entire set of all possible application scenarios, a small but representative subset of application scenarios is used to determine the fitness of mapping solutions. Since the representativeness of such a subset is dependent on the application mappings being explored, these representative subsets of application scenarios are dynamically obtained by means of coexploration of the scenario subset space. In this chapter, we provide an overview of scenario-based DSE and, in particular, present multiple techniques for fitness prediction using representative subsets of application scenarios: a stochastic, deterministic, and hybrid combination.

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Abbreviations

ASIC:

Application-Specific Integrated Circuit

ASIP:

Application-Specific Instruction-set Processor

DSE:

Design Space Exploration

ESL:

Electronic System Level

FIFO:

First-In First-Out

FS:

Feature Selection

GA:

Genetic Algorithm

JPEG:

Joint Photographic Experts Group

KPN:

Kahn Process Network

MJPEG:

Motion JPEG

MoC:

Model of Computation

MPSoC:

Multi-Processor System-on-Chip

SBS:

Sequential Backward Selection

SFS:

Sequential Forward Selection

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Author information

Authors and Affiliations

  1. University of Amsterdam, Science Park 904, Amsterdam, The Netherlands

    Andy Pimentel

  2. Philips Healthcare, Veenpluis 4, Best, The Netherlands

    Peter van Stralen

Authors
  1. Andy Pimentel
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  2. Peter van Stralen
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Corresponding author

Correspondence to Andy Pimentel .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul National University, Seoul, Korea (Republic of)

    Soonhoi Ha

  2. Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany

    Jürgen Teich

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Pimentel, A., van Stralen, P. (2017). Scenario-Based Design Space Exploration. In: Ha, S., Teich, J. (eds) Handbook of Hardware/Software Codesign. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7267-9_10

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