  Many artificial intelligence (AI) devices have been developed to accelerate
the training and inference of neural networks models. The most common ones are
the Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU). They are
highly optimized for dense data representations. However, sparse
representations such as graphs are prevalent in many domains, including
science. It is therefore important to characterize the performance of available
AI accelerators on sparse data. This work analyzes and compares the GPU and TPU
performance training a Graph Neural Network (GNN) developed to solve a
real-life pattern recognition problem. Characterizing the new class of models
acting on sparse data may prove helpful in optimizing the design of deep
learning libraries and future AI accelerators.
