Estimation of distribution-based multiobjective design space exploration for energy and throughput-optimized MPSoCs

Murad, Maryam; Hussain, Ishfaq; Ahmad, Ayaz; Qadri, Muhammad Yasir; Qadri, Nadia N.

doi:10.3906/elk-1812-59

Cited by 2 publications

(2 citation statements)

References 47 publications

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“…If we look on Table 3, we can see the corresponding configurations for throughput and energy consumption shown in Figure 1. For comparison, we also obtained the true Pareto front for the same ranges of configuration parameters and a Pareto front using the integer variant of NSGA-II [40] and EDA algorithm [14].…”

Section: Algorithm Resultsmentioning

confidence: 99%

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An Integer Cat Swarm Optimization Approach for Energy and Throughput Efficient MPSoC Design

Murtza¹,

Ahmad²,

Qadri³

et al. 2020

IJCIS

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Modern multicore architectures have an ability to allocate optimum system resources for a specific application to have improved energy and throughput balance. The system resources can be optimized automatically by using optimization algorithms. Stateof-the-art using optimization algorithm in the field of such architectures has shown promising results in terms of minimized energy consumption through configuration of number of CPU cores, limited cache sizes and operating frequency. We propose, in this work, a Cat Swarm Optimization (CSO) algorithm-based technique, Integer CSO (ICSO) for the design space exploration (DSE) of multicore computer architectures to find improved energy and throughput balance. The proposed integer variant of CSO algorithm demonstrates convergent behavior for all of design space parameters variations. The Pareto front proposed by ICSO is explored by using various SPLASH-2 benchmarks. Results show significant decrease in energy consumption without affecting throughput severely. Simulation results also validate the use of ICSO in DSE for multicore architectures.

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Section: Algorithm Resultsmentioning

confidence: 99%

“…Recent research has produced various DSE techniques in the field of Multiprocessor System on Chip (MPSoC) [14,15]. In the following, we provide an account of some of the state-of-the-art approaches in this area.…”

Section: Related Workmentioning

confidence: 99%

An Integer Cat Swarm Optimization Approach for Energy and Throughput Efficient MPSoC Design

Murtza¹,

Ahmad²,

Qadri³

et al. 2020

IJCIS

Self Cite

View full text Add to dashboard Cite

show abstract

GA–EDA: Hybrid Design Space Exploration Engine for Multicore Architecture

Waris

Ahmad

Qadri

et al. 2021

J CIRCUIT SYST COMP

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Emergence of modern multicore architectures has made runtime reconfiguration of system resources possible. All reconfigurable system resources constitute a design space and the proper selection of configuration of these resources to improve the system performance is known as Design Space Exploration (DSE). This reconfiguration feature helps in appropriate allocation of system resources to improve the efficiency in terms of performance, energy consumption, throughput, etc. Different techniques like exhaustive search of design space, architect’s experience, etc. are used for optimization of system resources to achieve desired goals. In this work, we hybridized two optimization algorithms, i.e., Genetic Algorithm (GA) and Estimation of Distribution Algorithm (EDA) for DSE of computer architecture. This hybrid algorithm achieved optimal balance between two objectives (minimal energy consumption and maximal throughput) by using decision variables such as number of cores, cache size and operating frequency. The final set of optimal solutions proposed by this GA–EDA hybrid algorithm is explored and verified by running different benchmark applications derived from SPLASH-2 benchmark suite on a cycle level simulator. The significant reduction in energy consumption without extensive impact on throughput in simulation results validate the use of this GA–EDA hybrid algorithm for DSE of multicore architecture. Moreover, the simulation results are compared with that of standalone GA, EDA and fuzzy logic to show the efficiency of GA–EDA hybrid algorithm.

show abstract

Estimation of distribution-based multiobjective design space exploration for energy and throughput-optimized MPSoCs

Cited by 2 publications

References 47 publications

An Integer Cat Swarm Optimization Approach for Energy and Throughput Efficient MPSoC Design

An Integer Cat Swarm Optimization Approach for Energy and Throughput Efficient MPSoC Design

GA–EDA: Hybrid Design Space Exploration Engine for Multicore Architecture

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