Name
Title | ||
|---|---|---|
A Unified Framework for Training, Mapping and Simulation of ReRAM-Based Convolutional Neural Network Acceleration |
Abstract | ||
|---|---|---|
ReRAM-based neural network accelerators (RNAs) could outshine their digital counterparts in terms of computational efficiency and performance remarkably. However, some open software tool for broad architectural exploration and end-to-end evaluation are still missing. We present a simulation framework of RNA for CNN inference that encompasses a ReRAM-aware NN training tool, a CNN-oriented mapper an... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LCA.2019.2908374 | IEEE Computer Architecture Letters |
Keywords | Field | DocType |
Computational modeling,Computer architecture,RNA,Training,Microprocessors,Artificial neural networks,Hardware | Computer architecture,Convolutional neural network,Inference,Computer science,Real-time computing,Integrated circuit design,Acceleration,Electronic circuit,Artificial neural network,Resistive random-access memory,Open software | Journal |
Volume | Issue | ISSN |
18 | 1 | 1556-6056 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Liu | 1 | 50 | 8.37 |
| Jianhui Han | 2 | 3 | 1.06 |
| Youhui Zhang | 3 | 202 | 28.36 |