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Hierarchical Dataflow Modeling of Iterative Applications: Deep Neural Network as a Case Study

Title
Hierarchical Dataflow Modeling of Iterative Applications: Deep Neural Network as a Case Study
Author
오현옥
Keywords
Dataflow; SDF graph; Loop parallelization; Code generation
Issue Date
2017-06
Publisher
ACM
Citation
DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017, Page. 1-6
Abstract
Even though dataflow models are good at exploiting task-level parallelism of an application, it is difficult to exploit the parallelism of loop structures since they are not explicitly specified in existent dataflow models. To overcome this drawback, we propose a novel extension to the SDF model, called SDF/L graph, specifying the loop structures explicitly in a hierarchical fashion. With a given SDF/L graph specification and the mapping and scheduling information, an application can be automatically parallelized on a multicore system. The enhanced expression capability by the proposed extension is verified with two applications, k-means clustering and deep neural network application.
URI
https://dl.acm.org/doi/abs/10.1145/3061639.3062260https://repository.hanyang.ac.kr/handle/20.500.11754/160748
DOI
10.1145/3061639.3062260
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INFORMATION SYSTEMS(정보시스템학과) > Articles
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