HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
This work addresses the problem of inefficient hardware acceleration for knowledge graph reasoning, offering a novel co-design solution that improves performance and energy efficiency for AI applications, though it is incremental in combining HDC with FPGA acceleration.
The paper tackles the challenge of accelerating Knowledge Graph Completion (KGC), which has high algorithm complexity, by proposing HDReason, an algorithm-hardware codesign using Hyperdimensional Computing (HDC) for efficient and acceleration-friendly KGC. The result is an FPGA-based accelerator that achieves an average 10.6x speedup and 65x energy efficiency improvement compared to an NVIDIA RTX 4090 GPU, with similar accuracy.
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.