Abstract:Quantization can be used to form new vectors/matrices with shared values close to the original. In recent years, the popularity of scalar quantization for value-sharing application has been soaring as it has been found huge utilities in reducing the complexity of neural networks. Existing clusteringbased quantization techniques, while being well-developed, have multiple drawbacks including the dependency of the random seed, empty or out-of-the-range clusters, and high time complexity for large number of cluste… Show more
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