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.
@article{arxiv.2202.11343,
title = {Alleviating Datapath Conflicts and Design Centralization in Graph Analytics Acceleration},
author = {Haiyang Lin and Mingyu Yan and Duo Wang and Mo Zou and Fengbin Tu and Xiaochun Ye and Dongrui Fan and Yuan Xie},
journal= {arXiv preprint arXiv:2202.11343},
year = {2022}
}
Comments
To Appear in 59th Design Automation Conference (DAC 2022)