HSBNN: A High-Scalable Bayesian Neural Networks Accelerator Based on Field Programmable Gate Arrays (FPGA)

Traditional artificial neural networks have inherent overfitting problems and tend to produce overly confident predictions due to their reliance on point estimation methods. In contrast, Bayesian theory offers a probabilistic framework that replaces point estimation with probability distributions, e...

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Veröffentlicht in:	Cognitive computation Jg. 17; H. 3; S. 100
Hauptverfasser:	Liu, Yinghao, Zhang, Hao, Sun, Zhe, Duan, Feng, Ma, Yuan, Lu, Wenyi, Caiafa, Cesar F., Solé-Casals, Jordi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2025 Springer Nature B.V
Schlagworte:	Artificial Intelligence Artificial neural networks Bayesian analysis Classification Computation Computation by Abstract Devices Computational Biology/Bioinformatics Computer Science Data processing Data transfer (computers) Edge computing Efficiency Field programmable gate arrays High acceleration Information sources Neural networks Normal distribution Optimization techniques Parallel processing Parameterization Pipelining (computers) Probability distribution Random numbers Statistical analysis Edge Computing Bio-inspired Neural Networks FPGA Bayesian Neural Networks Hardware Accelerator
ISSN:	1866-9956, 1866-9964
Online-Zugang:	Volltext
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