  In this paper, we propose IMA-GNN as an In-Memory Accelerator for centralized
and decentralized Graph Neural Network inference, explore its potential in both
settings and provide a guideline for the community targeting flexible and
efficient edge computation. Leveraging IMA-GNN, we first model the computation
and communication latencies of edge devices. We then present practical case
studies on GNN-based taxi demand and supply prediction and also adopt four
large graph datasets to quantitatively compare and analyze centralized and
decentralized settings. Our cross-layer simulation results demonstrate that on
average, IMA-GNN in the centralized setting can obtain ~790x communication
speed-up compared to the decentralized GNN setting. However, the decentralized
setting performs computation ~1400x faster while reducing the power consumption
per device. This further underlines the need for a hybrid semi-decentralized
GNN approach.
