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

Jia, Zai Jian; Pimentel, Andy D.; Thompson, Mark; Bautista, Tomás; Núñez, Antonio

doi:10.1109/estmed.2010.5666979

Cited by 39 publications

(26 citation statements)

References 18 publications

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“…A second class of design space pruning is based on hierarchical DSE (e.g., [7], [14], [10], [5]). In this approach, DSE is first performed using analytical or symbolic models to quickly find the interesting parts in the design space, after which simulation-based DSE is performed to more accurately search for the optimal design points.…”

Section: Resultsmentioning

confidence: 99%

Design space pruning through hybrid analysis in system-level design space exploration

Piscitelli

Pimentel

2012

2012 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Abstract-System-level design space exploration (DSE), which is performed early in the design process, is of eminent importance to the design of complex multi-processor embedded system architectures. During system-level DSE, system parameters like, e.g., the number and type of processors, the type and size of memories, or the mapping of application tasks to architectural resources, are considered. Simulation-based DSE, in which different design instances are evaluated using system-level simulations, typically are computationally costly. Even using high-level simulations and efficient exploration algorithms, the simulation time to evaluate design points forms a real bottleneck in such DSE. Therefore, the vast design space that needs to be searched requires effective design space pruning techniques. This paper presents a technique to reduce the number of simulations needed during system-level DSE. More specifically, we propose an iterative design space pruning methodology based on static throughput analysis of different application mappings. By interleaving these analytical throughput estimations with simulations, our hybrid approach can significantly reduce the number of simulations that are needed during the process of DSE.

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Section: Resultsmentioning

confidence: 99%

Design space pruning through hybrid analysis in system-level design space exploration

Piscitelli

Pimentel

2012

2012 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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“…NASA [13] is also a similar tool. It allows the user to easily integrate his/her DSE algorithm and offers the possibility to connect to any simulator (features offered also by FADSE).…”

Section: Related Workmentioning

confidence: 97%

Multi-objective optimisations for a superscalar architecture with selective value prediction

Gellért¹,

Calborean²,

Vintan³

et al. 2012

IET Comput. Digit. Tech.

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This work extends an earlier manual design space exploration (DSE) of the authors' developed selective load value prediction-based superscalar architecture to the L2 unified cache. After that the authors perform an automatic DSE using a special developed software tool by varying several architectural parameters. The goal is to find optimal configurations in terms of cycles per instruction and energy consumption. By varying 19 architectural parameters, as the authors proposed, the design space is over 2.5 millions of billions configurations which obviously means that only a heuristic search can be considered. Therefore the authors propose different methods of automatic DSE based on their developed framework for automatic design space exploration which allow them to evaluate only 2500 configurations of the above mentioned huge design space! The experimental results show that their automatic DSE provides significantly better configurations than the previous manual DSE approach, considering the proposed multi-objective approach.

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“…The authors of NASA [10] perform a DSE using single objective genetic algorithms for each objective. Multiple algorithms are used at the same time and a comparison between sets of these algorithms is made.…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Multi-objective Algorithms for the Automatic Design Space Exploration of a Superscalar System

Calborean

Jahr

Ungerer

et al. 2013

Advances in Intelligent Systems and Computing

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Abstract. In today's computer architectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms. In this paper we selected three of them, NSGA-II and SPEA2 as genetic algorithms as well as SMPSO as a particle swarm optimization, and compared their performance. As test case we optimize the parameters of the Grid ALU Processor (GAP) microarchitecture and then GAP together with the post-link code optimizer GAPtimize. An analysis of the simulation results shows a very good performance of all the three algorithms. SMPSO reveals the fastest convergence speed. A clear winner between NSGA-II and SPEA2 cannot be determined.

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

Cited by 39 publications

References 18 publications

Design space pruning through hybrid analysis in system-level design space exploration

Design space pruning through hybrid analysis in system-level design space exploration

Multi-objective optimisations for a superscalar architecture with selective value prediction

A Comparison of Multi-objective Algorithms for the Automatic Design Space Exploration of a Superscalar System

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