Compression of machine-learned models by vector quantization

Abstract

A computing system can include one or more processors and one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the computing system to perform operations including obtaining model structure data indicative of a plurality of parameters of a machine-learned model; determining a codebook comprising a plurality of centroids, the plurality of centroids having a respective index of a plurality of indices indicative of an ordering of the codebook; determining a plurality of codes respective to the plurality of parameters, the plurality of codes respectively comprising a code index of the plurality of indices corresponding to a closest centroid of the plurality of centroids to a respective parameter of the plurality of parameters; and providing encoded data as an encoded representation of the plurality of parameters of the machine-learned model, the encoded data comprising the codebook and the plurality of codes.