Infoscience

Conference paper

Fast Hierarchical NPN Classification

Classifying functions according to some common properties into libraries of functions is an important step in many logic synthesis and technology mapping algorithms used in FPGA design flows. NPN classification is one of the frequently used classifications. Existing algorithms for NPN classification perform a sequence of steps to derive the resulting NPN class, but discard the intermediate results produced at the end of each step. The hierarchical method introduced in this paper uses the same sequence of steps, but it saves the intermediate results at each step and reuses them when classifying other functions. It is, on average, 3.7 times faster compared to a state-of-the-art non- hierarchical method, at the cost of a modest increase in memory needed to save the class hierarchy. The hierarchical approach enables a rapid exact NPN classification for functions up to 10 inputs—it exactly classifies one million 6-input functions in the same time as the heuristic state-of-the-art algorithm.

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