Name
Abstract | ||
|---|---|---|
Convolutional-Neural-Network (CNN) is used in broad applications. There are dataflows for convolutional layers in CNN such as row-stationary and weight-stationary. However, these dataflows have strengths and weaknesses. This paper analyzed two representative dataflows and introduce the dataflow-reconfigurable CNN accelerator that takes advantage of both dataflows. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISOCC.2018.8649988 | 2018 International SoC Design Conference (ISOCC) |
Keywords | Field | DocType |
Energy efficiency,Random access memory,Convolutional neural networks,Convolution,Arrays | Convolution,Convolutional neural network,Efficient energy use,Computer science,Real-time computing,Dataflow,Computer engineering,Strengths and weaknesses | Conference |
ISSN | ISBN | Citations |
2163-9612 | 978-1-5386-7960-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Myungwoo Oh | 1 | 0 | 0.34 |
| Chaeeun Lee | 2 | 0 | 2.03 |
| Sanghun Lee | 3 | 6 | 3.85 |
| Youngho Seo | 4 | 0 | 0.34 |
| Sunwoo Kim | 5 | 4 | 4.20 |
| Jooho Wang | 6 | 0 | 0.34 |
| Chester Sungchung | 7 | 0 | 0.34 |