The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 PSO(입자 군집 최적화) 알고리즘으로 훈련된 신경망(NN)을 위한 하이브리드 아키텍처를 제시합니다. NN은 하드웨어 측에서 구현되는 반면 PSO는 소프트웨어 측 프로세서에 의해 실행됩니다. 또한 상관 정보를 줄이기 위해 주성분 분석(PCA)도 적용됩니다. PCA 모듈은 작동 속도를 높이기 위해 SystemVerilog 프로그래밍 언어로 하드웨어에 구현됩니다. 실험 결과는 제안된 아키텍처가 성공적으로 구현되었음을 보여주었다. 또한 PSO 프로그램으로 학습한 하드웨어 기반 NN(NN-PSO)이 PSO 프로그램으로 학습한 소프트웨어 기반 NN보다 속도가 더 빨랐습니다. 제안된 PCA가 있는 NN-PSO는 PCA가 없는 NN-PSO보다 더 나은 인식률을 얻었습니다.
Tuan Linh DANG
Hanoi University of Science and Technology
Yukinobu HOSHINO
Kochi University of Technology
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부
Tuan Linh DANG, Yukinobu HOSHINO, "Hardware-Based Principal Component Analysis for Hybrid Neural Network Trained by Particle Swarm Optimization on a Chip" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 10, pp. 1374-1382, October 2019, doi: 10.1587/transfun.E102.A.1374.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1374/_p
부
@ARTICLE{e102-a_10_1374,
author={Tuan Linh DANG, Yukinobu HOSHINO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Hardware-Based Principal Component Analysis for Hybrid Neural Network Trained by Particle Swarm Optimization on a Chip},
year={2019},
volume={E102-A},
number={10},
pages={1374-1382},
abstract={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.},
keywords={},
doi={10.1587/transfun.E102.A.1374},
ISSN={1745-1337},
month={October},}
부
TY - JOUR
TI - Hardware-Based Principal Component Analysis for Hybrid Neural Network Trained by Particle Swarm Optimization on a Chip
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1374
EP - 1382
AU - Tuan Linh DANG
AU - Yukinobu HOSHINO
PY - 2019
DO - 10.1587/transfun.E102.A.1374
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2019
AB - 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.
ER -