Compression of Machine-Learned Models by Vector Quantization

A computer-implemented method for compressing a machine-learned model, the method comprising:

• 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;
• wherein the plurality of parameters comprises a plurality of weights of at least one layer of the machine-learned model, the plurality of weights comprising a weight matrix of the at least one layer.