DAC 2024 paper on automated GNN design and deployment for device-edge co-inference systems

👏 Paper title: Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems.

This work studies how to design and deploy GNNs across a device-edge co-inference system. Running all computation on the device can exceed local resources, while offloading too much work can create communication bottlenecks, especially for graph workloads with irregular data dependencies.

The proposed framework treats model architecture and deployment mapping as a joint design problem. By modeling communication together with computation, it searches for GNN architectures and partitioning schemes that improve end-to-end efficiency rather than optimizing model accuracy in isolation.

This is important because GNN co-inference is shaped by both model structure and graph data movement. A design with high accuracy can still perform poorly if intermediate graph features or messages create too much device-edge communication.

By searching the architecture and mapping space together, the framework can find deployment choices that match the system constraints. It provides an early foundation for later device-edge GNN co-inference methods that optimize model design and execution placement as one problem.