  With the increasing popularity of graph-based learning, Graph Neural Networks
(GNNs) win lots of attention from the research and industry field because of
their high accuracy. However, existing GNNs suffer from high memory footprints
(e.g., node embedding features). This high memory footprint hurdles the
potential applications towards memory-constrained devices, such as the
widely-deployed IoT devices. To this end, we propose a specialized GNN
quantization scheme, SGQuant, to systematically reduce the GNN memory
consumption. Specifically, we first propose a GNN-tailored quantization
algorithm design and a GNN quantization fine-tuning scheme to reduce memory
consumption while maintaining accuracy. Then, we investigate the
multi-granularity quantization strategy that operates at different levels
(components, graph topology, and layers) of GNN computation. Moreover, we offer
an automatic bit-selecting (ABS) to pinpoint the most appropriate quantization
bits for the above multi-granularity quantizations. Intensive experiments show
that SGQuant can effectively reduce the memory footprint from 4.25x to 31.9x
compared with the original full-precision GNNs while limiting the accuracy drop
to 0.4% on average.
