Analysis of the Effect on the Quantization of the Network`s Outputs in the Neural Processor by the Implementation of Hybrid VLSI 


Vol. 9,  No. 4, pp. 429-436, Aug.  2002
10.3745/KIPSTB.2002.9.4.429


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  Abstract

In order to apply the artificial neural network to the practical application, it is needed to implement it with the hardware system. It is most promising to make it with the hybrid VLSI among various possible technologies. When we implement a trained network into the hybrid neuro-chips, it is to be performed the process of the quantization on its neuron outputs and its weights. Unfortunately this process cause the network´s outputs to be distorted from the original trained outputs. In this paper we analysed in detail the statistical characteristics of the distortion. The analysis implies that the network is to be trained using the normalized input patterns and finally into the solution with the small weights to reduce the distortion of the network´s outputs. We performed the expriment on an application in the time series prediction area to investigate the effectiveness of the results of the analysis. The experiment showed that the network by our method has more smaller distortion compared with the regular network.

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[IEEE Style]

O. J. Kwon, S. W. Kim, J. M. Lee, "Analysis of the Effect on the Quantization of the Network`s Outputs in the Neural Processor by the Implementation of Hybrid VLSI," The KIPS Transactions:PartB , vol. 9, no. 4, pp. 429-436, 2002. DOI: 10.3745/KIPSTB.2002.9.4.429.

[ACM Style]

Oh Jun Kwon, Seong Woo Kim, and Jong Min Lee. 2002. Analysis of the Effect on the Quantization of the Network`s Outputs in the Neural Processor by the Implementation of Hybrid VLSI. The KIPS Transactions:PartB , 9, 4, (2002), 429-436. DOI: 10.3745/KIPSTB.2002.9.4.429.

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