graph processing

edge devices

networks cnns

Graph Convolutional Networks (GCNs) have become state-of-the-art deep learning models for representation learning on graphs. Hardware acceleration of GCN inference is challenging due to: 1) massive size of the input graph, 2) heterogeneous workload of the GCN inference that consists of sparse and dense matrix operations, and 3) irregular information propagation along the edges during the computation. To address the above challenges, we propose the algorithm-architecture co-optimization to accelerate large-scale GCN inference on FPGA. We first perform data partitioning to fit each partition in the limited on-chip memory. Then, we use a two-phase preprocessing algorithm consisting of sparsification and node reordering. The first phase (sparsification) eliminates edge connections of high-degree nodes by merging common neighbor nodes. The second phase (re-ordering) effectively groups adjacent nodes to improve on-chip data reuse. Incorporating the above algorithmic optimizations, we propose a generic FPGA architecture to pipeline the two major computational kernels in GCN: aggregation and transformation. The flexible data path and task scheduling strategy of our design support various GCN models and lead to high throughput inference. We evaluate our design on state-of-the-art FPGA platform using three large scale datasets: Flickr, Reddit, Yelp. Compared with the state-of-the-art multi-core and GPU baselines, our design improves the throughput by up to $30 \times$ and $2 \times$ respectively.

Hardware Acceleration of Large Scale GCN Inference