technical report available

[Yun Xie, Sydey Univ. Elec. Eng., Tel: (+61-2]

The following is the abstract of a report on our recent research work. The
report is available by FTP and has been submitted.


        Analysis of the Effects of Quantization in Multi-Layer 
              Neural Networks Using Statistical Model
      
         Yun Xie                             Marwan A. Jabri

Dept. of Electronic Engineering        Shool of Electrical Engineering
Tsinghua University                    The University of Sydney  
Beijing 100084, P.R.China              N.S.W. 2006, Australia

                     ABSTRACT

    A statistical quantization model is used to analyse the effects
of quantization  when digital technique is used to implement a
real-valued feedforward multi-layer neural network. 
    In this process, we introduce a parameter that we call 
``effective non-linearity coefficient'' which is important in the study 
of the quantization effects.
    We develop, as function of the quantization parameters,  general 
statistical formulations of the performance degradation of the neural 
network caused by quantization.
    Our formulation predicts (as intuitively one may think) that network's
performance degradation gets worse when the number of bits is decreased; 
a change of the number of hidden units in a layer has no effect on the  
degradation; for a constant ``effective non-linearity coefficient'' and 
number of bits, an increase in the number of layers leads to  worse 
performance degradation of the network; the number of bits in successive 
layers can be reduced if the neurons of the lower layer are non-linear.




unix>ftp cheops.cis.ohio-state.edu
Connected to cheops.cis.ohio-state.edu
220 cheops.cis.ohio-state.edu FTP server ready.
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331 Guest login ok, send ident as password.
Password: neuron
230 Guest login ok, access restrictions apply.
ftp>binary
ftp>cd pub
ftp>cd neuroprose
ftp>get yun.quant.ps.Z
ftp>bye

unix>uncompress yun.quant.ps.Z
unix>lpr yun.quant.ps