Name
Abstract | ||
|---|---|---|
Processing in memory (PIM) has increasingly attracted wide interests from academy and industry with the emergence of novel memory technologies and big-data applications. As a consequence, recent work has proposed different types of PIM architectures for various fields. However, most of their evaluations rely on dedicated simulation models, which make it hard to compare different architectures. Analytical models of PIM architectures, as a solution, have not been fully discussed in literature. To help architects and programmers easily evaluate overheads of various PIM architectures, this paper presents V-PIM, a generic and flexible analytical model. By characterizing an architecture with only several parameters, the model is simple but effective. To validate the model, we compare the results of V-PIM with simulation results against three novel PIM architectures and four tasks from data-intensive to computational-intensive. The average error of V-PIM is 15.2% or 7.1% for time or energy overheads according to our evaluation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/NVMSA.2018.00026 | 2018 IEEE 7th Non-Volatile Memory Systems and Applications Symposium (NVMSA) |
Keywords | Field | DocType |
analytical model,processing in memory | Kernel (linear algebra),Architecture,Computer architecture,Task analysis,Computer science,Non-volatile memory,Overhead (business) | Conference |
ISSN | ISBN | Citations |
2575-2561 | 978-1-5386-7404-8 | 1 |
PageRank | References | Authors |
0.35 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peichen Xie | 1 | 10 | 1.30 |
| Guangyu Sun | 2 | 1920 | 111.55 |
| Feng Wang | 3 | 136 | 7.44 |
| Guojie Luo | 4 | 363 | 39.53 |