Degree-Quant: Quantization-Aware Training for Graph Neural Networks

Understand

Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data.

• Despite their promise, there exists little research exploring methods to make them more efficient at inference time.
• In this work, we explore the viability of training quantized GNNs, enabling the usage of low precision integer arithmetic during inference.
• We identify the sources of error that uniquely arise when attempting to quantize GNNs, and propose an architecturally-agnostic method, Degree-Quant, to improve performance over existing quantization-aware training baselines commonly used on other architectures, such as CNNs.