ACE-GCN is a fast and resource/energy-efficient FPGA accelerator for graph convolutional embedding under data-driven and in-place processing conditions. Our accelerator exploits the inherent power law distribution and high sparsity commonly exhibited by real-world graphs datasets. Contrary to other hardware implementations of GCN, on which traditional optimization techniques are employed to bypass the problem of dataset sparsity, our architecture is designed to take advantage of this very same situation. We propose and implement an innovative acceleration approach supported by our “implicit-processing-by-association” concept, in conjunction with a dataset-customized convolutional operator. The computational relief and consequential acceleration effect arise from the possibility of replacing rather complex convolutional operations for a faster embedding result estimation. Based on a computationally inexpensive and super-expedited similarity calculation, our accelerator is able to decide from the automatic embedding estimation or the unavoidable direct convolution operation. Evaluations demonstrate that our approach presents excellent applicability and competitive acceleration value. Depending on the dataset and efficiency level at the target, between 23× and 4,930× PyG baseline, coming close to AWB-GCN by 46% to 81% on smaller datasets and noticeable surpassing AWB-GCN for larger datasets and with controllable accuracy loss levels. We further demonstrate the unique hardware optimization characteristics of our approach and discuss its multi-processing potentiality.

中文翻译：

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ACE-GCN：用于 GCN 嵌入的快速数据驱动 FPGA 加速器

ACE-GCN 是一种快速且资源/节能的 FPGA 加速器，用于在数据驱动和就地处理条件下进行图卷积嵌入。我们的加速器利用了真实世界图形数据集通常表现出的固有幂律分布和高稀疏性。与使用传统优化技术来绕过数据集稀疏问题的 GCN 的其他硬件实现相反，我们的架构旨在利用同样的情况。我们提出并实施了一种创新的加速方法，由我们的“implicit-processing-by-association”概念支持，并结合数据集定制的卷积算子。计算缓解和相应的加速效果源于替换相当复杂的卷积运算以实现更快的嵌入结果估计的可能性。基于计算成本低且超快速的相似度计算，我们的加速器能够从自动嵌入估计或不可避免的直接卷积操作中做出决定。评估表明，我们的方法具有出色的适用性和具有竞争力的加速价值。根据目标的数据集和效率水平，在 23× 和 4,930× PyG 基线之间，在较小的数据集上接近 AWB-GCN 46% 到 81%，在较大的数据集上明显超过 AWB-GCN，并且具有可控的精度损失水平.