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English(EN) Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference

面向能效的FPGA图卷积网络（GCN）加速器协同优化稀疏化与近似计算

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的、面向边缘设备能效推理的图卷积网络（GCN）FPGA加速器。该系统在AMD Kria KV260平台上集成了图稀疏化与近似计算技术，特别是8位量化和近似乘法器。在Cora和Amazon Photo等数据集上的评估显示，在保持高分类精度的同时，功耗低于1W，速度提升高达9.88倍。研究强调，图稀疏化与近似算术的协同优化是实现高效低功耗GCN推理的关键。 AI

影响 使得图神经网络在边缘设备上能够更高效、更低功耗地部署。

排序理由 这是一篇关于FPGA上GCN推理的新型软硬件协同设计的学术论文。

在 Hugging Face Daily Papers 阅读 →

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面向能效的FPGA图卷积网络（GCN）加速器协同优化稀疏化与近似计算

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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    面向能效型FPGA GCN推理的图稀疏化与近似计算协同优化

    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 pre…

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