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Abstract
The size and complexity of graphs increase, the computational demands of GNNs become a major bottleneck. To address this challenge, researchers have explored hardware acceleration techniques to speed up GNN computations. This paper presents an overview- of existing hardware acceleration methods for GNNs, including specialized hardware designs and optimizations. We discuss the advantages and limitations of these approaches and highlight the key factors to consider when designing hardware accelerators for GNNs. Furthermore, we present potential directions for future research in this domain, aiming to unlock the full potential of GNNs through efficient hardware acceleration.
Researchers are exploring various approaches for hardware acceleration of GNNs, including custom-designed hardware, accelerators like GPUs and TPUs, and software optimizations for existing hardware. As this research area progresses, it has the potential to revolutionize how graph-based data is processed, enabling more advanced and efficient solutions across a wide range of industries and domains.
The COPRAS method requires identifying selection criteria, evaluating information related to these criteria, and developing methods to evaluate Meeting the participant's needs Criteria for doing in order to assess the overall performance of the surrogate. Decision analysis involves a Decision Maker (DM) Situation to do consider a particular set of alternatives and select one among several alternatives, usually with conflicting criteria. For this reason, the developed complexity proportionality assessment (COPRAS) method can be used.
Hardware acceleration of GNNS. QM is got the first rank whereas the live journal is having the Lowest rank.
