GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks
This work addresses performance bottlenecks in GCN accelerators for applications with relational data, representing an incremental improvement over prior methods.
The paper tackles the problem of inefficient data movement in Graph Convolutional Neural Networks (GCNs) by introducing GROW, a row-stationary sparse-dense GEMM accelerator, which achieves significant energy-efficiency improvements compared to state-of-the-art GCN accelerators.
Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational. A unique property of GCNs is that its two primary execution stages, aggregation and combination, exhibit drastically different dataflows. Consequently, prior GCN accelerators tackle this research space by casting the aggregation and combination stages as a series of sparse-dense matrix multiplication. However, prior work frequently suffers from inefficient data movements, leaving significant performance left on the table. We present GROW, a GCN accelerator based on Gustavson's algorithm to architect a row-wise product based sparse-dense GEMM accelerator. GROW co-designs the software/hardware that strikes a balance in locality and parallelism for GCNs, achieving significant energy-efficiency improvements vs. state-of-the-art GCN accelerators.