Key points are not available for this paper at this time.
FPGA has been an emerging computing infrastructure in datacenters benefiting from features of fine-grained parallelism, energy efficiency, and reconfigurability. Meanwhile, graph processing has attracted tremendous interest in data analytics, and its performance is in increasing demand with the rapid growth of data. Many works have been proposed to tackle the challenges of designing efficient FPGA-based accelerators for graph processing. However, the largely overlooked programmability still requires hardware design expertise and sizable development efforts from developers.
Chen et al. (2021) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: