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In this letter, a piecewise linear (PWL) sigmoid function approximation based on the statistical distribution probability of the neurons' values in each layer is proposed to improve the network recognition accuracy with only addition circuit. The sigmoid function is first divided into three fixed regions, and then according to the neurons' values distribution probability, the curve in each region is segmented into sub-regions to reduce the approximation error and improve the recognition accuracy. Experiments performed on Xilinx's FPGA-XC7A200T for MNIST and CIFAR-10 datasets show that the proposed method achieves 97.45% recognition accuracy in DNN, 98.42% in CNN on MNIST and 72.22% on CIFAR-10, up to 0.84%, 0.57% and 2.01% higher than other approximation methods with only addition circuit.

- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.9 pp.2023-2026

- Publication Date
- 2020/09/01

- Publicized
- 2020/06/11

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.2020EDL8007

- Type of Manuscript
- LETTER

- Category
- Biocybernetics, Neurocomputing

Vantruong NGUYEN

Xidian University

Jueping CAI

Xidian University

Linyu WEI

Xidian University

Jie CHU

Xidian University

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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Vantruong NGUYEN, Jueping CAI, Linyu WEI, Jie CHU, "Neural Networks Probability-Based PWL Sigmoid Function Approximation" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 9, pp. 2023-2026, September 2020, doi: 10.1587/transinf.2020EDL8007.

Abstract: In this letter, a piecewise linear (PWL) sigmoid function approximation based on the statistical distribution probability of the neurons' values in each layer is proposed to improve the network recognition accuracy with only addition circuit. The sigmoid function is first divided into three fixed regions, and then according to the neurons' values distribution probability, the curve in each region is segmented into sub-regions to reduce the approximation error and improve the recognition accuracy. Experiments performed on Xilinx's FPGA-XC7A200T for MNIST and CIFAR-10 datasets show that the proposed method achieves 97.45% recognition accuracy in DNN, 98.42% in CNN on MNIST and 72.22% on CIFAR-10, up to 0.84%, 0.57% and 2.01% higher than other approximation methods with only addition circuit.

URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8007/_p

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@ARTICLE{e103-d_9_2023,

author={Vantruong NGUYEN, Jueping CAI, Linyu WEI, Jie CHU, },

journal={IEICE TRANSACTIONS on Information},

title={Neural Networks Probability-Based PWL Sigmoid Function Approximation},

year={2020},

volume={E103-D},

number={9},

pages={2023-2026},

abstract={In this letter, a piecewise linear (PWL) sigmoid function approximation based on the statistical distribution probability of the neurons' values in each layer is proposed to improve the network recognition accuracy with only addition circuit. The sigmoid function is first divided into three fixed regions, and then according to the neurons' values distribution probability, the curve in each region is segmented into sub-regions to reduce the approximation error and improve the recognition accuracy. Experiments performed on Xilinx's FPGA-XC7A200T for MNIST and CIFAR-10 datasets show that the proposed method achieves 97.45% recognition accuracy in DNN, 98.42% in CNN on MNIST and 72.22% on CIFAR-10, up to 0.84%, 0.57% and 2.01% higher than other approximation methods with only addition circuit.},

keywords={},

doi={10.1587/transinf.2020EDL8007},

ISSN={1745-1361},

month={September},}

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TY - JOUR

TI - Neural Networks Probability-Based PWL Sigmoid Function Approximation

T2 - IEICE TRANSACTIONS on Information

SP - 2023

EP - 2026

AU - Vantruong NGUYEN

AU - Jueping CAI

AU - Linyu WEI

AU - Jie CHU

PY - 2020

DO - 10.1587/transinf.2020EDL8007

JO - IEICE TRANSACTIONS on Information

SN - 1745-1361

VL - E103-D

IS - 9

JA - IEICE TRANSACTIONS on Information

Y1 - September 2020

AB - In this letter, a piecewise linear (PWL) sigmoid function approximation based on the statistical distribution probability of the neurons' values in each layer is proposed to improve the network recognition accuracy with only addition circuit. The sigmoid function is first divided into three fixed regions, and then according to the neurons' values distribution probability, the curve in each region is segmented into sub-regions to reduce the approximation error and improve the recognition accuracy. Experiments performed on Xilinx's FPGA-XC7A200T for MNIST and CIFAR-10 datasets show that the proposed method achieves 97.45% recognition accuracy in DNN, 98.42% in CNN on MNIST and 72.22% on CIFAR-10, up to 0.84%, 0.57% and 2.01% higher than other approximation methods with only addition circuit.

ER -