@inproceedings{7bcb4406381a449db2900f13a2e33bfb,
title = "GraphiDe: A graph processing accelerator leveraging in-DRAM-computing",
abstract = "In this paper, we propose GraphiDe, a novel DRAM-based processing-in-memory (PIM) accelerator for graph processing. It transforms current DRAM architecture to massively parallel computational units exploiting the high internal bandwidth of the modern memory chips to accelerate various graph processing applications. GraphiDe can be leveraged to greatly reduce energy consumption and latency dealing with underlying adjacency matrix computations by eliminating unnecessary off-chip accesses. The extensive circuit-architecture simulations over three social network data-sets indicate that GraphiDe achieves on average 3.1x energy-efficiency improvement and 4.2x speed-up over the recent DRAM based PIM platform. It achieves ∼59x higher energy-efficiency and 83x speed-up over GPU-based acceleration methods.",
keywords = "Dram, In-memory computing",
author = "Shaahin Angizi and Deliang Fan",
note = "Funding Information: dblp-2010(amazon-2008a)dblp-2010(b)amazon-2008 Figure 8: (a) The memory bottleneck ratio and (b) resource utilization ratio. 6 CONCLUSION In this paper, we presented GraphiDe, which transforms current DRAM sub-arrays to massively parallel computational units to reduce energy consumption dealing with graph processing tasks and eliminate unnecessary off-chip accesses. The simulation results on three social network data-sets show GraphiDe can roughly achieve 3.1× energy-efficiency improvement and 4.2× speed-up over the recent processing-in-DRAM platform. It achieves ∼59× higher energy-efficiency and 83× speed-up over GPU-based acceleration methods. ACKNOWLEDGEMENTS This work is supported in part by the National Science Foundation under Grant No. 1740126 and Semiconductor Research Corporation nCORE. REFERENCES Publisher Copyright: {\textcopyright} 2019 ACM.; 29th Great Lakes Symposium on VLSI, GLSVLSI 2019 ; Conference date: 09-05-2019 Through 11-05-2019",
year = "2019",
month = may,
day = "13",
doi = "10.1145/3299874.3317984",
language = "English (US)",
series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",
publisher = "Association for Computing Machinery",
pages = "45--50",
booktitle = "GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI",
}