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We present a technique and algorithms to solve the following problem: Given both a Neural Network trained to classify a set of images, along with a set of floating-point hardware blocks (in reconfigurable logic), find the arrangement of blocks that achieves the best mix of precision, resources and speed with respect to a given cost function. We first illustrate the technique in detail by using a small example, then show that it may be used for a larger problem, bar code classification.