Czasopismo | ELEKTRONIKA - KONSTRUKCJE, TECHNOLOGIE, ZASTOSOWANIA | Rocznik 2011 - zeszyt 12

A Flexible Winner Takes All Neural Network with the conscience mechanism realized on microcontrollers

nr katalogowy: 64765

Implementation of ANNs on microcontrollers (μC) is an interesting alternative in comparison with PC-based realizations of such networks [1, 2]. The achievable data rates are in this case usually smaller than in case of NNs realized on PC, but in many applications high data rate is not the most important, while small sizes of a device and relatively low power dissipation are the paramount features. On the other hand, in comparison with the realizations of ANNs as VLSI ASICs that offer ultra low power dissipation and large density and, in consequence, very small device sizes [3, 4], the ANNs realized on μC are much more flexible in terms of reconfigurability. The core blocks in this case are digital that makes such systems much easier in the realization. In this case there is no influence of negative effects present in analog implementation, e.g. the current leakage and the charge injection effects [3]. Winner Takes All ANNs belong to the group of networks trained without the supervision. The training process, in each learning cycle, starts in this case with presenting the network particular learning patterns X from an input data set. For each pattern the network calculates a distance between the X and the weights W vectors in all neurons. Different measures of the similarity between the X and the W vectors are being used. The most popular are the Euclidean (L2) and the Manhattan (L1) measures defined as follows: (1) (2) In the L1 case both the squaring and the rooting operations have been eliminated that significantly simplifies the overall learning algorithm. Both these measures have been used in the proposed implementation of the ANN, for the comparison. Detailed investigations carried out by the authors with various data sets and different numbers of neurons show that the L1 measure offers comparable results, while it requires much less computational resources. The adaptation process of the winning neurons is per[...]

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