Abstract
Graph Neural Network (GNN) has super strong graph learning and reasoning ability, so it has attracted more and more attention. The GNN combines the characteristics of graph computing and traditional neural networks, and can execute some complex, non-Euclidean space data, forming a mixed calculation mode of irregular and regular. The traditional processor structure design cannot satisfy the simultaneous processing of graph calculation and neural network acceleration. Therefore, this article proposes a dedicated acceleration architecture for GNNs, customizes hardware computing units and on-chip storage methods, optimizes calculations and memory access, and implements them on the Ultra96-V2 board. The experimental results show that the accelerator designed in this article has achieved good acceleration effect.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
