@inproceedings{8e53504b91424c50a4d25fae43bac061,
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 = "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",
}