VLSI Technologies for Artificial Neural Networks

Goser K, Hilleringmann U, Rückert U, Schumacher K (1989)
IEEE Micro 9(6): 28-44.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Goser, Karl; Hilleringmann, Ulrich; Rückert, UlrichUniBi; Schumacher, Klaus
Abstract / Bemerkung
VLSI systems, basic integrated circuits, and silicon technologies are discussed. Novel circuit and design principles that provide a foundation for the implementation of a wide variety of neural network models in silicon are described. The key issues for a successful integration of neural systems are identified. The realization of algorithms in silicon is examined. Special-purpose hardware for carrying out the activation and transfer function and for the connection elements is discussed. A brief overview of the current silicon technologies is provided.
Erscheinungsjahr
1989
Zeitschriftentitel
IEEE Micro
Band
9
Ausgabe
6
Seite(n)
28-44
ISSN
0272-1732
Page URI
https://pub.uni-bielefeld.de/record/2280945

Zitieren

Goser K, Hilleringmann U, Rückert U, Schumacher K. VLSI Technologies for Artificial Neural Networks. IEEE Micro. 1989;9(6):28-44.
Goser, K., Hilleringmann, U., Rückert, U., & Schumacher, K. (1989). VLSI Technologies for Artificial Neural Networks. IEEE Micro, 9(6), 28-44. https://doi.org/10.1109/40.42985
Goser, Karl, Hilleringmann, Ulrich, Rückert, Ulrich, and Schumacher, Klaus. 1989. “VLSI Technologies for Artificial Neural Networks”. IEEE Micro 9 (6): 28-44.
Goser, K., Hilleringmann, U., Rückert, U., and Schumacher, K. (1989). VLSI Technologies for Artificial Neural Networks. IEEE Micro 9, 28-44.
Goser, K., et al., 1989. VLSI Technologies for Artificial Neural Networks. IEEE Micro, 9(6), p 28-44.
K. Goser, et al., “VLSI Technologies for Artificial Neural Networks”, IEEE Micro, vol. 9, 1989, pp. 28-44.
Goser, K., Hilleringmann, U., Rückert, U., Schumacher, K.: VLSI Technologies for Artificial Neural Networks. IEEE Micro. 9, 28-44 (1989).
Goser, Karl, Hilleringmann, Ulrich, Rückert, Ulrich, and Schumacher, Klaus. “VLSI Technologies for Artificial Neural Networks”. IEEE Micro 9.6 (1989): 28-44.
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