In recent times, a plethora of hardware accelerators have been put forth for graph learning applications such as vertex classification and graph classification. However, previous works have paid little attention to Knowledge Graph Completion (KGC), a task that is well-known for its significantly higher algorithm complexity. The state-of-the-art KGC solutions based on graph convolution neural network (GCN) involve extensive vertex/relation embedding updates and complicated score functions, which are inherently cumbersome for acceleration. As a result, existing accelerator designs are no longer optimal, and a novel algorithm-hardware co-design for KG reasoning is needed. Recently, brain-inspired HyperDimensional Computing (HDC) has been introduced as a promising solution for lightweight machine learning, particularly for graph learning applications. In this paper, we leverage HDC for an intrinsically more efficient and acceleration-friendly KGC algorithm. We also co-design an acceleration framework named HDReason targeting FPGA platforms. On the algorithm level, HDReason achieves a balance between high reasoning accuracy, strong model interpretability, and less computation complexity. In terms of architecture, HDReason offers reconfigurability, high training throughput, and low energy consumption. When compared with NVIDIA RTX 4090 GPU, the proposed accelerator achieves an average 10.6x speedup and 65x energy efficiency improvement. When conducting cross-models and cross-platforms comparison, HDReason yields an average 4.2x higher performance and 3.4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.

翻译：近年来，针对顶点分类和图分类等图学习应用，研究者提出了大量硬件加速器。然而，先前工作鲜少关注知识图谱补全（KGC）这一以算法复杂度显著更高而著称的任务。基于图卷积神经网络（GCN）的最优KGC方案涉及大量的顶点/关系嵌入更新和复杂的评分函数，这些特性本质上不利于加速。因此，现有加速器设计已不再最优，亟需面向知识图谱推理的新型算法-硬件协同设计。最近，受脑启发的超维计算（HDC）被引入作为轻量级机器学习（特别是图学习应用）的潜在解决方案。本文利用HDC开发了一种内在更高效且易于加速的KGC算法，并协同设计了面向FPGA平台的HDReason加速框架。在算法层面，HDReason实现了高推理准确率、强模型可解释性与低计算复杂度之间的平衡。在架构层面，HDReason具备可重构性、高训练吞吐量和低能耗特性。与NVIDIA RTX 4090 GPU相比，所提加速器平均实现10.6倍加速比和65倍能效提升。进行跨模型与跨平台对比时，与基于FPGA的最优GCN训练平台相比，HDReason在相似准确率下平均获得4.2倍性能提升和3.4倍能效提升。