• DocumentCode
    288476
  • Title

    Linear quantization of Hebbian-type associative memories in interconnection implementations

  • Author

    Chung, Pau-Choo ; Tsai, Ching-Tsorng ; Sun, Yung-Nien

  • Author_Institution
    Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
  • Volume
    2
  • fYear
    1994
  • fDate
    27 Jun-2 Jul 1994
  • Firstpage
    1092
  • Abstract
    The effects of linear quantized Hebbian-type associative memories (HAMs) on storage capacity and hardware implementations are explored in this paper. For the linear quantization, the interconnection weights are linearly quantized into a small number of levels. This consideration focuses mainly on the situation when only a limited accuracy range can be achieved on hardware implementations. Results of simulation and theory show that the number of quantization levels required is relatively small compared with the possible values of interconnections. Therefore, linear quantization in HAMs is worthwhile in hardware implementations
  • Keywords
    Hebbian learning; content-addressable storage; neural chips; neural nets; quantisation (signal); HAMs; Hebbian-type associative memories; hardware implementations; interconnection implementations; interconnection weights; linear quantization; linear quantized Hebbian-type associative memories; quantization levels; simulation; storage capacity; Analog circuits; Associative memory; Digital circuits; Hardware; Hebbian theory; Integrated circuit interconnections; Neurons; Quantization; Sun; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1901-X
  • Type

    conf

  • DOI
    10.1109/ICNN.1994.374335
  • Filename
    374335