Name
Abstract | ||
|---|---|---|
The fast, efficient processing of graphs is needed to quickly analyze and understand connected data, from large social network graphs, to edge devices performing timely, local data analytics. But, as graph data tends to exhibit poor locality, designing both high-performance and efficient graph accelerators have been difficult to realize. In this work, GraphWave, we take a different approach compared to previous research and focus on maximizing accelerator parallelism with a compute-at-memory approach, where each vertex is paired with a dedicated functional unit. We also demonstrate that this work can improve performance and efficiency by optimizing the accelerator's interconnect with multi-level multicasting to minimize congestion. Taken together, this work achieves, to the best of our knowledge, a state-of-the-art efficiency of up to 63.94 GTEPS/W with a throughput of 97.80 GTEPS (billion traversed edges per second). |
Year | DOI | Venue |
|---|---|---|
2022 | 10.23919/DATE54114.2022.9774535 | 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE) |
Keywords | DocType | ISSN |
accelerator parallelism,GraphWave,highly-parallel compute-at-memory graph processing accelerator,social network graphs,data analytics,edge devices,accelerator interconnect,multilevel multicasting,DRAM | Conference | 1530-1591 |
ISBN | Citations | PageRank |
978-1-6654-9637-7 | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinho Lee | 1 | 0 | 0.68 |
| Burin Amornpaisannon | 2 | 0 | 1.01 |
| Tulika Mitra | 3 | 2714 | 135.99 |
| Trevor E. Carlson | 4 | 411 | 27.09 |