Hardware acceleration framework for graph neural network quantization

Abstract

This application describes an hardware and a software design for quantization in GNN computation. An exemplary method may include: receiving a graph comprising a plurality of nodes respectively represented by a plurality of feature vectors; segmenting the plurality of feature vectors into a plurality of sub-vectors and grouping the plurality of sub-vectors into a plurality of groups of sub-vectors; performing vector clustering on each of the plurality of groups of sub-vectors to generate a plurality of centroids as a codebook; encoding each of the plurality of feature vectors to obtain a plurality of index maps by quantizing sub-vectors within the each feature vector based on the codebook, wherein each index map occupies a smaller storage space than the each feature vector does; and storing the plurality of index maps as an assignment table instead of the plurality of feature vectors to represent the plurality of nodes for GNN computation.