An FPGA-Based Accelerated Optimization Algorithm for Real-Time Applications

Psarakis, Mihalis; Dounis, Anastasios I.; Almabrok, Abdoalnasir; Stavrinidis, S.; Gkekas, Georgios

doi:10.1007/s11265-020-01522-5

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…MLP-Jaya consistently achieves convergence in fewer iterations across most datasets. It reaches the convergence criterion (e.g., validation accuracy above 0.95 or 1.0) in either the first iteration or a very low number of iterations, makes it more suitable to real-time application [34]. MLP-Jaya can achieve rapid convergence due to Jaya's ability to direct the population towards the best solution and away from the worst solution [35].…”

Section: Resultsmentioning

confidence: 99%

Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification

Novika,

Girsang

2024

IJEECS

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This study introduces an innovative hyperparameter optimization approach for enhancing multilayer perceptrons (MLP) using the Jaya algorithm. Addressing the crucial role of hyperparameter tuning in MLP’s performance, the Jaya algorithm, inspired by social behavior, emerges as a promising optimization technique without algorithm-specific parameters. Systematic application of Jaya dynamically adjusts hyperparameter values, leading to notable improvements in convergence speeds and model generalization. Quantitatively, the Jaya algorithm consistently achieves convergences at first iteration, faster convergence compared to conventional methods, resulting in 7% higher accuracy levels on several datasets. This research contributes to hyperparameter optimization, offering a practical and effective solution for optimizing MLP in diverse applications, with implications for improved computational efficiency and model performance.

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Section: Resultsmentioning

confidence: 99%

Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification

Novika,

Girsang

2024

IJEECS

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“…This approach improved design efficiency and increased data throughput. In [31], a pipelined design of the Big Bang-Big Crunch algorithm using HLS was proposed. This design improved system performance.…”

Section: Background a Related Workmentioning

confidence: 99%

FPGA-Based Implementation of Ship Detection for Satellite On-Board Processing

Chen

Shi

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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At present, the raw echo data captured by spaceborne SAR is usually downlinked to the ground stations for imaging and analysis. If the SAR data can be processed on-board, the processing results could be transmitted to users directly through broadcast distribution, which can greatly reduce the delay time for emergency applications. However, on-board processing implementation faces many challenges due to the space radiation environment and limited resources of the satellite. In this paper, an FPGA-based implementation of ship detection for on-board processing is proposed, which has high efficiency in logic and memory resources. First, the hardware-oriented clustering approach is proposed. Image processing operations with similar regularity of data access can be mapped in one processing engine, which can reduce the employment of logic resources. Second, the concept of the data-buffering cycle (DBC) is proposed. The DBC provides a method to analyze the intermediate data and optimize the memory reuse. Finally, based on the above optimization methods, the FPGA-based implementation for a ship detection algorithm is presented. Compared with the traditional methods, the experimental results show the efficacy of our proposed method with lower consumption in logic and memory resources.

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“…The proposed solution is universal and allows one to speed up calculations in situations where there is limited computing power and time to perform the studies. This parallelization allows for an acceleration of calculations, for example, on GPU or FPGA [38][39][40][41][42], which is a current and much needed task. The special case of MultiPDF PF for N f = 1 is the simple bootstrap particle filter.…”

Section: Multipdf Particle Filtermentioning

confidence: 99%

MultiPDF particle filtering in state estimation of nonlinear objects

et al. 2021

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This paper presents a new particle filter algorithm (MultiPDF) for state estimation of nonlinear systems. The proposed method is a modification of the standard particle filter approach. Due to the strong need for the acceleration of calculations and an improvement in the estimation quality of state estimation, the authors propose a method which enables one to divide the main particle filter into smaller sub-filters with an accordingly smaller number of particles for each one of them. The algorithm has been implemented for various numbers of particles and subordinate parallel filters. Estimation quality has been checked for nine nonlinear objects (both one- and multidimensional) and evaluated through the quality index, average root-mean-squared error. The computation time of the particle filter algorithm for several hardware configurations has been compared. Based on the obtained results, it can be concluded that, besides the computation acceleration, the parallelization of the particle filter’s operation also improves the estimation quality.

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An FPGA-Based Accelerated Optimization Algorithm for Real-Time Applications

Cited by 14 publications

References 33 publications

Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification

Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification

FPGA-Based Implementation of Ship Detection for Satellite On-Board Processing

MultiPDF particle filtering in state estimation of nonlinear objects

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