Presentation
Brain-Like Hyper-Dimensional Graph Learning System with Hardware-Efficient Adaptive Sparsity
DescriptionGraph neural networks (GNNs) are crucial for numerous applications, yet their huge computational demands often lead to suboptimal performance.
Hyper-Dimensional Computing (HDC) is a brain-inspired learning approach for efficient and robust learning.
HDC-based Graph Learning (HDGL) shows significant improved computational efficiency and accuracy by learning graph representations in a high-dimensional space. Despite these advantages, general-purpose computing platforms such as CPUs and GPUs are insufficient for efficiently handling HDGL tasks.
In this paper we propose an accelerator called HDGAS through algorithm and hardware co-design for HDGL.
Based on the insight that not all node features in a graph are equally important, we propose to jointly optimize a lightweight filter with the HDGL model to dynamically identify and eliminate less significant node features during runtime.
Moreover, we design a specialized system architecture for end-to-end HDGL acceleration, harnessing the proposed dynamic sparsification technique in tandem with the inherent SpMM operations within HDGL.
Extensive experiments demonstrate that HDGAS achieves $6.76\times$ ($69.31\times$) speedup and $7.58\times$ ($80.12\times$) energy-efficiency improvements over GNN accelerators (GPU).
Hyper-Dimensional Computing (HDC) is a brain-inspired learning approach for efficient and robust learning.
HDC-based Graph Learning (HDGL) shows significant improved computational efficiency and accuracy by learning graph representations in a high-dimensional space. Despite these advantages, general-purpose computing platforms such as CPUs and GPUs are insufficient for efficiently handling HDGL tasks.
In this paper we propose an accelerator called HDGAS through algorithm and hardware co-design for HDGL.
Based on the insight that not all node features in a graph are equally important, we propose to jointly optimize a lightweight filter with the HDGL model to dynamically identify and eliminate less significant node features during runtime.
Moreover, we design a specialized system architecture for end-to-end HDGL acceleration, harnessing the proposed dynamic sparsification technique in tandem with the inherent SpMM operations within HDGL.
Extensive experiments demonstrate that HDGAS achieves $6.76\times$ ($69.31\times$) speedup and $7.58\times$ ($80.12\times$) energy-efficiency improvements over GNN accelerators (GPU).
Event Type
Research Manuscript
TimeMonday, July 271:30pm - 1:42pm PDT
LocationMtg Room 203AB