Heterogeneous memory-based hardware and system software framework for accelerating graph neural networks

Heterogeneous memory-based hardware and system software framework for accelerating graph neural networks


Graph Neural Networks(GNN), which can infer embeddings of untrained graphs, are emerging graph machine learning algorithms that are widely used in recommendation systems, lidars, social computing, and various natural science research that process large amounts of data. Since GNNs use large-scale non-Euclidean data to learn and deduce, they have very high accuracy, versatility, and compatibility with other graph processing systems.


Despite the advantages, the lack of specialized acceleration systems in GNNs and the lack of hardware design have significantly hindered the accessibility and the absence of GNN accelerations. To address the limitations, research is widely performed in areas including devices, circuits, and systems. However, performing research in individual fields fails to innovatively extend and improve the range of the performance and applications of GNNs.


The final goal of the work is to explore and study devices, circuits, and computer architectures vertically, design non-memory/memory semiconductors that specialize in AI, and develop low-power/high-performance AI hardware-software platforms to accelerate the emerging GNNs. Three GNN core technologies include 1) accelerator framework: designing data floor-based, heterogeneous accelerated hardware accelerator control framework that includes the development of versatile programming model for graph machine learning and GNN layer placement. 2) GNN hardware acceleration: redesigning accelerated processing circuits for accelerating GNN computation and designing ReRAM-based heterogeneous core for large ReRAM-based storage classes and improvement of area/power efficiency. 3)New heterogeneous non-volatile memory devices: developing three-terminal devices ReRAM device/memory array with byte input/output, multi-level, and high-capacity non-volatile characteristics and developing reliable two-terminal ReRAM device/memory array for sequential computation.


The development of AI-specific non-memory/memory semiconductors and low-power/high-performance AI hardware-software platform technologies in this research is expected to dominate high-value markets through technologies of integrated AI platforms.

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