GCoDE accepted by IEEE TC: architecture-mapping co-search for efficient device-edge GNN co-inference

👏 Paper title: GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search.

GCoDE targets efficient GNN inference across device-edge systems. GNN workloads are challenging for co-inference because graph partitions, message passing, model architecture, and communication overhead all interact with one another.

The framework jointly searches neural architectures and deployment mappings instead of optimizing them separately. By modeling computation, communication, and graph placement together, GCoDE avoids designs that are accurate but communication-heavy or efficient but accuracy-limited, improving the practicality of GNN serving across constrained devices and edge servers.

GCoDE is motivated by the close coupling between graph neural network structure and distributed execution cost. Changing the architecture affects intermediate features and computation patterns, while changing the mapping affects communication and latency.

By co-searching both dimensions, the framework can discover designs that are better suited to the actual device-edge system. This makes GCoDE a more holistic approach to GNN co-inference than methods that only tune the model or only tune the deployment plan.