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This paper evaluates the contribution of various microprocessor architectural features on the execution of 4 neural networks used for classification problems. In this study, we selected the grnn, pnn, mnn and rbfn networks trained for the Iris data set and simulated with 10,000 elements datasets. Using a superscalar simulator we evaluated various architectural parameters such as IPC, memory hierarchy,...
Classification of remote sensing image and range data is normally done in 2D space, because anyhow most sensors capture the surface of the earth from a close-to vertical direction and thus vertical structures, e.g. at building façades are not visible anyways. However, when the objects of interest are photographed from off-nadir directions, like in oblique airborne images, the question on how to efficiently...
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