Roofline-Model-Based Design Space Exploration for Dataflow Techniques of CNN Accelerators

Roofline-Model-Based Design Space Exploration for Dataflow Techniques of CNN Accelerators

To effectively compute convolutional layers, a complex design space must exist (e.g., the dataflow techniques associated with the layer parameters, loop transformation techniques, and hardware parameters). For efficient design space exploration (DSE) of various dataflow techniques, namely, the weigh...

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Bibliographic Details
Main Authors: Chan Park, Sungkyung Park, Chester Sungchung Park
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Accelerator
convolutional neural networks (CNNs)
dataflow techniques
roofline
simulation
processing element (PE)
Online Access:https://ieeexplore.ieee.org/document/9201450/

Internet

https://ieeexplore.ieee.org/document/9201450/

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