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DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks

IEEE transactions on neural networks and learning systems, v.29 no.5, 2018년, pp.1441 - 1453  

Kim, Lok-Won (Department of Computer Science and Engineering, Kyung Hee University, Seoul, South Korea)

Abstract AI-Helper 아이콘AI-Helper

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used ...

참고문헌 (29)

  1. Maeda, Y., Tada, T.. FPGA implementation of a pulse density neural network with learning ability using simultaneous perturbation. IEEE transactions on neural networks, vol.14, no.3, 688-695.

  2. Oh, Kyoung-Su, Jung, Keechul. GPU implementation of neural networks. Pattern recognition, vol.37, no.6, 1311-1314.

  3. Jung, Seul, Kim, Sung su. Hardware Implementation of a Real-Time Neural Network Controller With a DSP and an FPGA for Nonlinear Systems. IEEE transactions on industrial electronics : a publication of the IEEE Industrial Electronics Society, vol.54, no.1, 265-271.

  4. Holt, J.L., Hwang, J.-N.. Finite precision error analysis of neural network hardware implementations. IEEE transactions on computers, vol.42, no.3, 281-290.

  5. Proc Int Conf Mach Learn Bitwise neural networks kim 2015 6 

  6. Deep Learning with Limited Numerical Precision gupta 2015 

  7. 10.1109/FPL.2009.5272559 

  8. 10.1145/2684746.2689060 

  9. 10.1145/1553374.1553486 

  10. Le Ly, Daniel, Chow, Paul. High-Performance Reconfigurable Hardware Architecture for Restricted Boltzmann Machines. IEEE transactions on neural networks, vol.21, no.11, 1780-1792.

  11. Neural networks on GPUs: Restricted Boltzmann machines ly 2008 

  12. Mohamed, A., Dahl, G. E., Hinton, G.. Acoustic Modeling Using Deep Belief Networks. IEEE transactions on audio, speech, and language processing, vol.20, no.1, 14-22.

  13. An asynchronous parallel stochastic coordinate descent algorithm liu 2013 

  14. Liu, Y., Zhou, S., Chen, Q.. Discriminative deep belief networks for visual data classification. Pattern recognition, vol.44, no.10, 2287-2296.

  15. Proc 3rd Workshop Neural Networks Review of hardware neural networks: A users perspective lindsey 1994 26 

  16. 10.1145/1508128.1508140 

  17. Salakhutdinov, Ruslan, Hinton, Geoffrey. Semantic hashing. International journal of approximate reasoning : official publication of the North American Fuzzy Information Processing Society, vol.50, no.7, 969-978.

  18. Dias, Fernando Morgado, Antunes, Ana, Mota, Alexandre Manuel. Artificial neural networks: a review of commercial hardware. Engineering applications of artificial intelligence, vol.17, no.8, 945-952.

  19. Proc Intl Conf Field Programmable Logic and Appl FPGA implementations of neural networks—A survey of a decade of progress zhu 2003 1062 

  20. 10.1109/ICASSP.2011.5947494 

  21. Boser, B.E., Sackinger, E., Bromley, J., Le Cun, Y., Jackel, L.D.. An analog neural network processor with programmable topology. IEEE journal of solid-state circuits, vol.26, no.12, 2017-2025.

  22. Hinton, Geoffrey E., Osindero, Simon, Teh, Yee-Whye. A Fast Learning Algorithm for Deep Belief Nets. Neural computation, vol.18, no.7, 1527-1554.

  23. 10.1109/FCCM.2010.38 

  24. Memisevic, Roland, Hinton, Geoffrey E.. Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines. Neural computation, vol.22, no.6, 1473-1492.

  25. Kim, Lok-Won, Asaad, Sameh, Linsker, Ralph. A Fully Pipelined FPGA Architecture of a Factored Restricted Boltzmann Machine Artificial Neural Network. ACM transactions on reconfigurable technology and systems, vol.7, no.1, 1-23.

  26. Tausworthe, Robert C.. Random numbers generated by linear recurrence modulo two. Mathematics of computation, vol.19, no.90, 201-209.

  27. Amin, H., Curtis, K.M., Hayes-Gill, B.R.. Piecewise linear approximation applied to nonlinear function of a neural network. IEE proceedings: Circuits, devices and systems, vol.144, no.6, 313-317.

  28. Lecture Notes in Computer Science Efficient backprop lecun 1998 10.1007/3-540-49430-8_2 9 

  29. Hinton, G. E., Salakhutdinov, R. R.. Reducing the Dimensionality of Data with Neural Networks. Science, vol.313, no.5786, 504-507.

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