Efficient Acceleration of Stencil Applications through In-Memory Computing
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Sensors Lab
Date
2020-06-26Submitted Date
2020-06-03Permanent link to this record
http://hdl.handle.net/10754/663933
Metadata
Show full item recordAbstract
The traditional computer architectures severely suffer from the bottleneck between the processing elements and memory that is the biggest barrier in front of their scalability. Nevertheless, the amount of data that applications need to process is increasing rapidly, especially after the era of big data and artificial intelligence. This fact forces new constraints in computer architecture design towards more data-centric principles. Therefore, new paradigms such as in-memory and near-memory processors have begun to emerge to counteract the memory bottleneck by bringing memory closer to computation or integrating them. Associative processors are a promising candidate for in-memory computation, which combines the processor and memory in the same location to alleviate the memory bottleneck. One of the applications that need iterative processing of a huge amount of data is stencil codes. Considering this feature, associative processors can provide a paramount advantage for stencil codes. For demonstration, two in-memory associative processor architectures for 2D stencil codes are proposed, implemented by both emerging memristor and traditional SRAM technologies. The proposed architecture achieves a promising efficiency for a variety of stencil applications and thus proves its applicability for scientific stencil computing.Citation
Yantır, H. E., Eltawil, A. M., & Salama, K. N. (2020). Efficient Acceleration of Stencil Applications through In-Memory Computing. Micromachines, 11(6), 622. doi:10.3390/mi11060622Publisher
MDPI AGJournal
Micromachinesae974a485f413a2113503eed53cd6c53
10.3390/mi11060622
Scopus Count
Except where otherwise noted, this item's license is described as Archived with thanks to Micromachines