  Previous graph analytics accelerators have achieved great improvement on
throughput by alleviating irregular off-chip memory accesses. However, on-chip
side datapath conflicts and design centralization have become the critical
issues hindering further throughput improvement. In this paper, a general
solution, Multiple-stage Decentralized Propagation network (MDP-network), is
proposed to address these issues, inspired by the key idea of trading latency
for throughput. Besides, a novel High throughput Graph analytics accelerator,
HiGraph, is proposed by deploying MDP-network to address each issue in
practice. The experiment shows that compared with state-of-the-art accelerator,
HiGraph achieves up to 2.2x speedup (1.5x on average) as well as better
scalability.
