Title :
Analog feedforward neural networks with very low precision weights
Author :
Alibeik, Shahram Abdollahi ; Nemati, Farid ; Sharif-Bakhtiar, Mehrdad
Author_Institution :
Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran
Abstract :
An off chip training algorithm for feedforward neural networks is presented. This algorithm has been successfully used to train networks with weight precision as low as 1 bit. The effect of reducing the weight precision on the generalization ability of the network is presented. The network performance, in the presence of hardware non-idealities, has also been investigated. It is shown that a network with low precision weights can well tolerate the effect of hardware non-idealities if the network is properly trained
Keywords :
feedforward neural nets; generalisation (artificial intelligence); learning (artificial intelligence); neural chips; analog feedforward neural networks; generalization; low precision weights; off chip training; Analog circuits; Analog computers; Application software; Computer networks; Concurrent computing; Feedforward neural networks; High performance computing; Neural network hardware; Neural networks; Pattern recognition;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487908
