Hardware-Based Principal Component Analysis for Hybrid Neural Network Trained by Particle Swarm Optimization on a Chip

Tuan Linh DANG  Yukinobu HOSHINO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.10   pp.1374-1382
Publication Date: 2019/10/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.1374
Type of Manuscript: PAPER
Category: Neural Networks and Bioengineering
neural network,  principal component analysis,  particle swarm optimization,  hardware-based,  system on chip,  

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This paper presents a hybrid architecture for a neural network (NN) trained by a particle swarm optimization (PSO) algorithm. The NN is implemented on the hardware side while the PSO is executed by a processor on the software side. In addition, principal component analysis (PCA) is also applied to reduce correlated information. The PCA module is implemented in hardware by the SystemVerilog programming language to increase operating speed. Experimental results showed that the proposed architecture had been successfully implemented. In addition, the hardware-based NN trained by PSO (NN-PSO) program was faster than the software-based NN trained by the PSO program. The proposed NN-PSO with PCA also obtained better recognition rates than the NN-PSO without-PCA.