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
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374335