Accelerating large scale GCN inference on FPGA

Abstract

We propose an algorithm-architecture co-optimization framework to accelerate large-scale GCN inference on FPGA. We first perform data partitioning to fit each partition in the limited on-chip memory of FPGA. Then, we use the two-phase pre-processing 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 densely connected neighborhoods to improve on-chip data reuse. Incorporating the above algorithmic optimizations, we propose an FPGA architecture to efficiently execute the two key computational kernels of GCN - feature aggregation and weight transformation. We evaluate our design on a state-of-the-art FPGA device. Compared with multi-core and GPU baselines, our design reduces the inference latency by up to 30x and 2x respectively.

Publication
2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)
Hanqing Zeng
Hanqing Zeng
PhD candidate in Computer Engineering

I design models, algorithms and systems for large scale graph learning.