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An efficient handwritten digit recognition method on a flexible parallel architecture

This paper presents neural and hybrid (symbolic and subsymbolic) applications downloaded on the distributed computer architecture ArMenX. This machine is articulated around a ring of FPGAs acting as routing resources as well as fine grain computing resources and thus giving great flexibility. More coarse grain computing resources-Transputer and DSP-tightly coupled via FPGAs give a large application spectrum to the machine, making it possible to implement heterogeneous algorithms efficiently involving both low level (computing intensive) and high level (control intensive) tasks. We first introduce the ArMenX project and the main architecture features. Then, after giving details on the computing of propagation and back-propagation of the multi-layer perceptron on ArMenX, we will focus on a handwritten digit (issued from a zip code data base) recognition application. An original and efficient method, involving three neural networks, is developed. The first two neural networks deal with the 'reading process', and the last neural network, which learned to write, helps to make decisions on the first two network outputs, when they are not confident. Before concluding, the paper presents the work of integration of ArMenX into a high level programming environment, designed to make it easier to take advantage of the architecture flexibility.