EE Professor Myoungsoo Jung’s research team has developed, for the first time, an AI semiconductor technology that can accelerate end-to-end inference for graph neural network-based machine learning.
Graphs are data structures composed of vertices and edges, each representing data points and their relationships. Graph-based neural networks, or Graph Neural Networks (GNNs), can learn complex relationships in real-world data, making them essential across applications such as recommender systems, social network services (SNS), and knowledge graphs. Despite their higher accuracy, GNN-based services have faced challenges in real-world deployments due to their high latency.
The research team found that 70–90% of GNN inference time is due to the graph preprocessing stage, where graph data structures are transformed, rather than the GNN model computation time itself. By analyzing preprocessing operations, the research team further identified the algorithms that conventional GPU architectures struggle to parallelize, and accelerated them using specialized hardware logic. Notably, their design separates a fixed-function hardware “shell” from a reconfigurable hardware “kernel,” enabling the kernel to be reconfigured on-the-fly to match the diverse input graph currently being processed and thus sustain high performance on dynamic scenarios.
To validate AutoGNN, the team built an RTL-based prototype on an FPGA and compared GNN inference performance against a server-grade Intel CPU and a high-end NVIDIA GPU. AutoGNN achieved 9.0× speedup over the CPU and 2.1× over the GPU, while reducing energy consumption by 3.3×. They also verified that, in realistic scenarios where graphs change in real time, the reconfigurable design can consistently maintain high performance.
This research, along with the paper titled “AutoGNN: End-to-End Hardware-Driven Graph Preprocessing for Enhanced GNN Performance”, was presented at ‘32nd IEEE International Symposium on High-Performance Computer Architecture (HPCA 2026)’.
The research was supported by the Samsung Future Technology Development Program (삼성미래기술육성사업), and further details are available on the lab website: `https://camelab.org`.
