Using low-power platforms for Evolutionary Multi-Objective Optimization algorithms

Moreno, Juan José Hurtado; Ortega, Gloria; Filatovas, Ernestas; Martínez, J.A.

doi:10.1007/s11227-016-1862-0

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…In designing CPU and GPU acceleration strategies, tasks are assigned based on the computational characteristics of the CPU and GPU [38][39][40]. The CPU is suitable for a series of control-type tasks, particularly those requiring low latency and high performance, such as data transfer and image display processes.…”

Section: Central Processing Unit (Cpu) and Gpu Processingmentioning

confidence: 99%

“…The CPU and GPU performances can be optimized by disabling the power-saving functions of the CPU, adjusting the GPU clock to the maximum frequency, and closing all superfluous programs. Due to the independent CPU and GPU memories on computers, there are many migrations between them; the CPU and GPU of the Jetson Nano Developer Kit share physically unified memory, eliminating the tedious transfer process [35][36][37][38][39][40].…”

Section: Central Processing Unit (Cpu) and Gpu Processingmentioning

confidence: 99%

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Fast and accurate phase processing in off-axis digital holography combining adaptive spatial filtering and an embedded GPU platform

Bai,

Li,

Sun

et al. 2024

Meas. Sci. Technol.

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Parallel-phase processing enables rapid phase extraction from off-axis digital holograms. To achieve fast and accurate results, the phase reconstruction processes were parallelized using improved filter algorithms and optimized programming strategies. First, an adaptive filtering method based on the Chan-Vese (CV) model which better suits parallelism was designed to extract the +1 term spectrum. We selected suitable computer unified device architecture (CUDA) libraries according to the characteristics of the key phase reconstruction steps. Acceleration technologies, such as virtual memory and shared memory, were used to improve the computational efficiency. Furthermore, we combined an improved 4f optical imaging system with an embedded graphic processing unit (GPU) platform to design a low-cost phase reconstruction system for off-axis digital holography. To verify the feasibility of our method, the reconstructed quality of the CV filtering method was estimated, and the run times of phase retrieval on the central processing unit (CPU) and embedded GPU were compared for off-axis holograms with different pixel sizes. Additionally, the dynamic fluctuation phase maps of water droplet evaporation were retrieved to demonstrate the real-time capability of the method.

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Section: Central Processing Unit (Cpu) and Gpu Processingmentioning

confidence: 99%

Section: Central Processing Unit (Cpu) and Gpu Processingmentioning

confidence: 99%

Fast and accurate phase processing in off-axis digital holography combining adaptive spatial filtering and an embedded GPU platform

Bai,

Li,

Sun

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Embedded systems, which are small size, reduced complexity and low-power architectures, can be used in the development of parallel efficient MOEAs [31].…”

Section: Implementation Issues Of Parallel Moeasmentioning

confidence: 99%

A unified view of parallel multi-objective evolutionary algorithms

Talbi

2019

Journal of Parallel and Distributed Computing

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Classification of the Technological Process Condition Based on Hybrid Neural Networks

Puchkov

Дли

Lobaneva

2021

Studies in Systems, Decision and Control

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Using low-power platforms for Evolutionary Multi-Objective Optimization algorithms

Cited by 9 publications

References 31 publications

Fast and accurate phase processing in off-axis digital holography combining adaptive spatial filtering and an embedded GPU platform

Fast and accurate phase processing in off-axis digital holography combining adaptive spatial filtering and an embedded GPU platform

A unified view of parallel multi-objective evolutionary algorithms

Classification of the Technological Process Condition Based on Hybrid Neural Networks

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