A Fully Probabilistic Model for Sigmoid Approximation and Its Hardware- Efficient Implementation

The sigmoid function is a representative activation function in shallow neural networks. Its hardware realization is challenging due to the complex exponential and reciprocal operations. Existing studies applied piecewise models to approximate sigmoid function and employed numerical methods or non-u...

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Veröffentlicht in:	IEEE transactions on circuits and systems. I, Regular papers Jg. 71; H. 8; S. 3775 - 3786
Hauptverfasser:	Lu, Wenhao, Lu, Minshan, Zhang, Xiangfen, Lu, Zhongzhiguang, Sun, Miao, Dong, Boyi, Shu, Zhou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Approximation Artificial neural networks ASIC implementation Complexity Computational modeling FPGA implementation Hardware hardware efficiency Integrated circuit modeling Localization method Neural networks Numerical methods Numerical models Optimization Probabilistic logic Probabilistic models probabilistic-based method Probability theory Random variables sigmoid activation function Statistical analysis Theorems
ISSN:	1549-8328, 1558-0806
Online-Zugang:	Volltext
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