协同优化图稀疏化与近似计算以实现基于FPGA的能效GCN推理
Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference
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- Trinity College Dublin（都柏林圣三一学院）
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中文总结 AI 辅助
针对边缘FPGA上GCN推理的能耗问题，提出结合DSpar稀疏化、8位量化与近似乘法器的加速器，在KV260上实现最高9.88倍加速并保持86.6%准确率，验证了稀疏化与近似计算的互补协同优化。
中文摘要 AI 辅助
图卷积网络（GCN）已成为从图结构数据中学习的强大框架，然而，由于稀疏图聚合的计算和内存需求，其在资源受限的边缘平台上的部署仍然具有挑战性。本工作提出了一种基于FPGA的GCN加速器，该加速器在AMD Kria KV260上结合了DSpar图稀疏化、8位量化和近似乘法器。在Cora、LastFM Asia和Amazon Photo数据集上进行了评估，该设计探索了稀疏化与近似在密度差异很大的图之间的相互作用。结果表明，近似算术的有效性受GCN计算中累积深度的支配。当应用于稀疏聚合操作时，近似乘法器最为有效，而图稀疏化通过减少聚合深度进一步提高了其可行性。这种组合方法在Amazon Photo上实现了高达9.88倍的加速，同时保持了86.6%的分类准确率，在Cora上实现了1.52倍的加速和77.0%的准确率，总功耗低于1瓦。这些结果表明，图稀疏化和近似计算是互补的技术，它们的协同优化能够在边缘FPGA平台上实现高效的低功耗GCN推理。
英文摘要
Graph Convolutional Networks (GCNs) have emerged as a powerful framework for learning from graph-structured data, yet their deployment on resource-constrained edge platforms remains challenging due to the computational and memory demands of sparse graph aggregation. This work presents an FPGA-based GCN accelerator that combines DSpar graph sparsification, 8-bit quantization, and approximate multipliers on the AMD Kria KV260. Evaluated on Cora, LastFM Asia, and Amazon Photo, the design explores the interaction between sparsification and approximation across graphs with widely varying densities. Results show that the effectiveness of approximate arithmetic is governed by accumulation depth within GCN computations. Approximate multipliers are most effective when applied to sparse aggregation operations, while graph sparsification further improves their viability by reducing aggregation depth. The combined approach achieves up to 9.88$\times$ speedup while maintaining 86.6\% classification accuracy on Amazon Photo, and 1.52$\times$ speedup with 77.0\% accuracy on Cora, with total power consumption below 1 W. These results demonstrate that graph sparsification and approximate computing are complementary techniques whose co-optimization enables efficient low-power GCN inference on edge FPGA platforms.
