AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
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by
Tong Geng, Ang Li, Tianqi Wang, Chunshu Wu, Yanfei Li, Runbin Shi, Antonino Tumeo, Shuai Che, Steve Reinhardt, Martin Herbordt
2020
Abstract
Deep learning systems have been applied mostly to Euclidean data such as
images, video, and audio. In many applications, however, information and their
relationships are better expressed with graphs. Graph Convolutional Networks
(GCNs) appear to be a promising approach to efficiently learn from graph data
structures, having shown advantages in many critical applications. As with
other deep learning modalities, hardware acceleration is critical. The
challenge is that real-world graphs are often extremely large and unbalanced;
this poses significant performance demands and design challenges.
In this paper, we propose Autotuning-Workload-Balancing GCN (AWB-GCN) to
accelerate GCN inference. To address the issue of workload imbalance in
processing real-world graphs, three hardware-based autotuning techniques are
proposed: dynamic distribution smoothing, remote switching, and row remapping.
In particular, AWB-GCN continuously monitors the sparse graph pattern,
dynamically adjusts the workload distribution among a large number of
processing elements (up to 4K PEs), and, after converging, reuses the ideal
configuration. Evaluations are performed using an Intel D5005 FPGA with five
commonly-used datasets. Results show that 4K-PE AWB-GCN can significantly
elevate the average PE utilization (from 32.5% to 88.6%) and demonstrate
considerable performance speedups over CPUs (7569x), GPUs (80.3x), and a prior
GCN accelerator (7.4x).
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