Yon D, Frith CD. Precision and the Bayesian brain. Curr Biol. 2021;31(17):R1026–32.
Opanasenko V, Fazilov SK, Radjabov S, Kakharov SS. Multilevel face recognition system. Cybern Syst Anal. 2024;60(1):146–51.
Tarnpradab S, Poonpinij P, Na Lumpoon N, Wattanapongsakorn N. Real-time masked face recognition and authentication with convolutional neural networks on the web application. Multimed Tools Appl; 2024. pp. 1–25.
Soumya, A., Cenkeramaddi, L. R., Chalavadi, V., et al. Multi-class object classification using deep learning models in automotive object detection scenarios. In: Sixteenth international conference on machine vision (ICMV 2023); 2024. vol. 13072, pp. 48–55, SPIE
Wang B, Wang P, Zhang Y, Wang X, Zhou Z, Wang Y. Condition-guided urban traffic co-prediction with multiple sparse surveillance data. IEEE Transactions on Vehicular Technology; 2024.
Chen J, Wang H, He E. A transfer learning-based CNN deep learning model for unfavorable driving state recognition. Cogn Comput. 2024;16(1):121–30.
Ichikawa K, Kaneko K. Bayesian inference is facilitated by modular neural networks with different time scales. PLoS Comput Biol. 2024;20(3): e1011897.
Gou H, Zhang G, Medeiros EP, Jagatheesaperumal SK, de Albuquerque VHC. A cognitive medical decision support system for IoT-based human-computer interface in pervasive computing environment. Cogn Comput. 2024;16(5):2471–86.
Chen Y, Ding Y, Hu Z-Z, and Ren Z. Geometrized task scheduling and adaptive resource allocation for large-scale edge computing in smart cities. IEEE Internet of Things Journal; 2025
Wang Z, Goudarzi M, Gong M, Buyya R. Deep reinforcement learning-based scheduling for optimizing system load and response time in edge and fog computing environments. Future Gen Comput Syst. 2024;152:55–69.
Alsharif MH, Jahid A, Kannadasan R, Singla MK, Gupta J, Nisar KS, Abdel-Aty A-H, Kim M-K. Survey of energy-efficient fog computing: techniques and recent advances. Energy Rep. 2025;13:1739–63.
Morán A, Canals V, Galan-Prado F, Frasser CF, Radhakrishnan D, Safavi S, Rosselló JL. Hardware-optimized reservoir computing system for edge intelligence applications. Cogn Comput; 2023. pp. 1–9
Tasci M, Istanbullu A, Tumen V, Kosunalp S. FPGA-QNN: quantized neural network hardware acceleration on FPGAs. Appl Sci. 2025;15(2):688.
Wang D, Xu K, Jiang D. PipeCNN: an OpenCL-based open-source FPGA accelerator for convolution neural networks. In: 2017 International conference on field programmable technology (ICFPT); 2017. pp. 279–282
Liu Z, Liu Q, Yan S, Cheung RC. An efficient FPGA-based depthwise separable convolutional neural network accelerator with hardware pruning. ACM Trans Reconfigurable Technol Syst. 2024;17(1):1–20.
Liu F, Li H, Hu W, He Y. Review of neural network model acceleration techniques based on FPGA platforms. Neurocomputing; 2024. 128511 .
Asiatici M, Maiorano D, Ienne P. How many CPU cores is an FPGA worth? Lessons learned from accelerating string sorting on a CPU-FPGA system. J Signal Process Syst. 2021;93:1405–17.
López-Asunción S, Ituero P. Enabling efficient on-edge spiking neural network acceleration with highly flexible FPGA architectures. Electronics. 2024;13(6):1074.
Wan Y, Chen J, Yang X, Zhang H, Huang C, Xie X. DSA-CNN: an FPGA-integrated deformable systolic array for convolutional neural network acceleration. Appl Intell. 2025;55(1):1–18.
Bai L, Zhao Y, Huang X. A CNN accelerator on FPGA using depthwise separable convolution. IEEE Transactions on Circuits and Systems II: Express Briefs. 2018;65(10):1415–9.
Fan H, Ferianc M, Rodrigues M, Zhou H, Niu X, Luk W. High-performance FPGA-based accelerator for Bayesian neural networks. In: 2021 58th ACM/IEEE design automation conference (DAC); 2021. pp. 1063–1068. IEEE
Ferianc M, Que Z, Fan H, Luk W, Rodrigues M. Optimizing Bayesian recurrent neural networks on an FPGA-based accelerator. In: 2021 International conference on field-programmable technology (ICFPT); 2021. pp. 1–10. IEEE.
Li H, Wan B, Fang Y, Li Q, Liu JK, An L. An FPGA implementation of Bayesian inference with spiking neural networks. Front Neurosci. 2024;17:1291051.
Cai R, Ren A, Liu N, Ding C, Wang L, Qian X, Pedram M, Wang Y. VIBNN: hardware acceleration of Bayesian neural networks. SIGPLAN Not. 2018;53:476–88.
Wu X, Wen C, Wang Z, Liu W, Yang J. A novel ensemble-learning-based convolution neural network for handling imbalanced data. Cogn Comput. 2024;16(1):177–90.
Rezaeezade A, Batina L. Regularizers to the rescue: fighting overfitting in deep learning-based side-channel analysis. J Cryptographic Eng. 2024;14(4):609–29.
Grabinski J, Gavrikov P, Keuper J, Keuper M. Robust models are less over-confident. Adv Neural Inf Process Syst. 2022;35:39059–75.
Le Coz A, Herbin S, Adjed F. Confidence calibration of classifiers with many classes. Adv Neural Inf Process Syst. 2024;37:77686–725.
Shridhar K, Laumann F, Liwicki M. A comprehensive guide to Bayesian convolutional neural network with variational inference; 2019. arXiv:1901.02731.
Pham N, Fomel S. Uncertainty estimation using Bayesian convolutional neural network for automatic channel detection. In: SEG international exposition and annual meeting, D031S068R001, SEG; 2020.
Graves, A. Practical variational inference for neural networks. In: Advances in neural information processing systems; 2011. pp. 2348–2356 .
Blei DM, Kucukelbir A, McAuliffe JD. Variational inference: a review for statisticians. J Am Stat Assoc. 2017;112(518):859–77.
Article MathSciNet Google Scholar
Kendall, A. and Gal, Y. What uncertainties do we need in Bayesian deep learning for computer vision?. In: Advances in neural information processing systems; 2017. pp. 5574–5584 .
Kwon Y, Won J-H, Kim BJ, Paik MC. Uncertainty quantification using Bayesian neural networks in classification: application to biomedical image segmentation. Comput Stat & Data Anal. 2020;142.
Stone JE, Gohara D, Shi G. OpenCL: a parallel programming standard for heterogeneous computing systems. Comput Sci & Eng. 2010;12(3):66.
Breyer, M., Van Craen, A., and Pflüger, D. A comparison of SYCL, OpenCL, CUDA, and OpenMP for massively parallel support vector machine classification on multi-vendor hardware. In: Proceedings of the 10th International Workshop on OpenCL; 2022. pp. 1–12.
Rychlik, Z. A central limit theorem for sums of a random number of independent random variables. In: Colloquium Mathematicum,. 35(1)147–158, Institute of Mathematics Polish Academy of Sciences 1976.
Angus JE. The probability integral transform and related results. SIAM Rev. 1994;36(4):652–4.
Article MathSciNet MATH Google Scholar
Box GEP, Muller ME. A note on the generation of random normal deviates; 1958.
Condo C, Gross W. Pseudo-random Gaussian distribution through optimised LFSR permutations. Electron Lett. 2015;51(25):2098–100.
Jr, P. R. P. “central limit theorem” in “probability, random variables and random signal principles” 4th ed., 125, 2001.
Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika. 1965;52:591–611.
Article MathSciNet MATH Google Scholar
D’Agostino R, Pearson ES. Tests for departure from normality. Biometrika. 1973;60:613–22.
MathSciNet MATH Google Scholar
LeCun Y, Cortes C, Burges C. MNIST handwritten digit database; 2010.
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X. TensorFlow: large-scale machine learning on heterogeneous systems; 2015. Software available from tensorflow.org.
Amdahl, G. M. Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18-20, 1967, Spring Joint Computer Conference, pp 483–485.
Gustafson JL. Reevaluating Amdahl’s law. Commun. ACM; 1988. pp. 532–533.
Li J, Yang SX. A novel feature learning-based bio-inspired neural network for real-time collision-free rescue of multirobot systems. IEEE Transactions on Industrial Electronics; 2024.
Wang Z, Li S, Xuan J, Shi T. Biologically inspired compound defect detection using a spiking neural network with continuous time-frequency gradients. Adv Eng Inf. 2025;65: 103132.
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