Title :
Error analysis of quantized weights for feedforward neural networks (FNN)
Author :
Wu, Duanpei ; Gowdy, J.N.
Author_Institution :
Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA
Abstract :
When a neural network is implemented with limited precision hardware, errors from the quantization of weights become important factors to be considered. In this paper, the authors present several analysis results based on general FNN structures and use several examples to examine the relation between weight errors and output classifications. A lower bound for L, the number of bits used to quantize the weights, is derived in the worst case. This paper also includes the detailed analysis of AND-gates
Keywords :
error analysis; feedforward neural nets; error analysis; feedforward neural networks; lower bound; output classifications; quantized weights; weight errors; worst case; Computer errors; Computer networks; Equations; Error analysis; Feedforward neural networks; Large-scale systems; Neural network hardware; Neural networks; Quantization; Training data;
Conference_Titel :
Southeastcon '94. Creative Technology Transfer - A Global Affair., Proceedings of the 1994 IEEE
Conference_Location :
Miami, FL
Print_ISBN :
0-7803-1797-1
DOI :
10.1109/SECON.1994.324361
