Name
Title | ||
|---|---|---|
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI |
Abstract | ||
|---|---|---|
Recent research demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication—the intensive and key computation in deep neural networks (DNNs). However, hardware failure, such as stuck-at-fault defects, is one of the main concerns that impedes the ReRAM devices to be a feasible so... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISQED51717.2021.9424332 | 2021 22nd International Symposium on Quality Electronic Design (ISQED) |
Keywords | DocType | ISSN |
Performance evaluation,Fault tolerance,Fault tolerant systems,Resistive RAM,Neural networks,Hardware,Task analysis | Conference | 1948-3287 |
ISBN | Citations | PageRank |
978-1-7281-7641-3 | 2 | 0.36 |
References | Authors | |
0 | 14 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geng Yuan | 1 | 9 | 3.80 |
| Zhiheng Liao | 2 | 2 | 2.39 |
| Xiaolong Ma | 3 | 9 | 3.46 |
| Yuxuan Cai | 4 | 2 | 2.05 |
| Zhenglun Kong | 5 | 4 | 2.77 |
| Xuan Shen | 6 | 2 | 0.36 |
| Jingyan Fu | 7 | 2 | 0.36 |
| Zhengang Li | 8 | 15 | 7.27 |
| Chengming Zhang | 9 | 5 | 3.10 |
| Hongwu Peng | 10 | 6 | 1.47 |
| Ning Liu | 11 | 2 | 0.70 |
| Ao Ren | 12 | 96 | 11.53 |
| Jinhui Wang | 13 | 4 | 3.78 |
| Yanzhi Wang | 14 | 1082 | 136.11 |