HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge
  Graph Reasoning
Hanning Chen Yang Ni Ali Zakeri Zhuowen Zou Sanggeon Yun Fei Wen Behnam Khaleghi Narayan Srinivasa Hugo Latapie Mohsen Imani
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.