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Volumn 24, Issue 1-2, 1998, Pages 75-87

Novel architecture and synapse design for hardware implementations of neural networks

Author keywords

Hardware implementation; Neural network; VLSI

Indexed keywords


EID: 0346906920     PISSN: 00457906     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0045-7906(97)00043-8     Document Type: Article
Times cited : (9)

References (21)
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    • Nosratinia A, Ahmadi M Sridhar M, Performance analysis and improvements on a hybrid cascade architecture for multi-layer neural networks. In Proc. of the 35th Midwest Symposium on Circuits and Systems. Vol. 2, 9-12 August 1992, pp. 1214-1217.
    • (1992) Proc. of the 35th Midwest Symposium on Circuits and Systems , vol.2 , pp. 9-12
    • Nosratinia, A.1    Ahmadi, M.2    Sridhar, M.3
  • 9
    • 0347753127 scopus 로고    scopus 로고
    • Nakamoto T, Takagi H, Moriizumi T. An analog back propagation circuit. Trans. Inst. Electronics, Information and Communication Engineers D-11 1992; J75D-II IEE:1:128-36.
    • J75D-II IEE , vol.1 , pp. 128-136
  • 12
    • 36149031331 scopus 로고
    • Learning in feedforward layer networks, the Tiling algorithm
    • Mezard M, Nadal J P. Learning in feedforward layer networks, the Tiling algorithm. J. Phys. 1989;A22:2191-203.
    • (1989) J. Phys. , vol.A22 , pp. 2191-2203
    • Mezard, M.1    Nadal, J.P.2
  • 13
    • 0346492450 scopus 로고
    • Modeling optimum architectures for VLSI implementations of artificial neural networks
    • Maynooth
    • McGinnity T M, McDaid L J, Campbell J G. Modeling optimum architectures for VLSI implementations of artificial neural networks, Fifth Irish Neural Networks Conference, Maynooth, 143-151 1995.
    • (1995) Fifth Irish Neural Networks Conference , pp. 143-151
    • McGinnity, T.M.1    McDaid, L.J.2    Campbell, J.G.3
  • 18
    • 0024861871 scopus 로고
    • Approximation by superposition of a sigmoidal function
    • Cybenko G. Approximation by superposition of a sigmoidal function. Math. Control, Signals System 1989;2:303-14.
    • (1989) Math. Control, Signals System , vol.2 , pp. 303-314
    • Cybenko, G.1
  • 19
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    • How neural networks learn from experience
    • September
    • Hinton G E. How neural networks learn from experience. Sci. Amer. 1992;September:105-26.
    • (1992) Sci. Amer. , pp. 105-126
    • Hinton, G.E.1
  • 20
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    • Geometrical and structural properties of systems of linear equations with applications in pattern recognition
    • Cover T. Geometrical and structural properties of systems of linear equations with applications in pattern recognition. IEEE Trans. Electron. Computing 1965;14:326-34.
    • (1965) IEEE Trans. Electron. Computing , vol.14 , pp. 326-334
    • Cover, T.1
  • 21
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    • The space of interactions in neural networks
    • Gardner E. The space of interactions in neural networks. J. Phys. 1988;A21:257-70.
    • (1988) J. Phys. , vol.A21 , pp. 257-270
    • Gardner, E.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.