HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform
Yi-Chien Lin, Bingyi Zhang, Viktor K. Prasanna
Abstract
Graph Neural Networks (GNNs) have shown great success in many applications such as recommendation systems, molecular property prediction, traffic prediction, etc. Recently, CPU-FPGA heterogeneous platforms have been used to accelerate many applications by exploiting customizable data path and abundant user-controllable on-chip memory resources of FPGAs. Yet, accelerating and deploying GNN training on such platforms requires not only expertise in hardware design but also substantial development efforts.
Topics & Concepts
Field-programmable gate arrayComputer scienceThroughputEmbedded systemComputer architecturePath (computing)Computer networkOperating systemWirelessAdvanced Graph Neural NetworksFerroelectric and Negative Capacitance DevicesGraph Theory and Algorithms