Name
Title | ||
|---|---|---|
Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning. |
Abstract | ||
|---|---|---|
Resistive RAM (RRAM) has been presented as a promising memory technology toward deep neural network (DNN) hardware design, with nonvolatility, high density, high ON/OFF ratio, and compatibility with logic process. However, prior RRAM works for DNNs have shown limitations on parallelism for in-memory computing, array efficiency with large peripheral circuits, multilevel analog operation, and demons... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MM.2019.2943047 | IEEE Micro |
Keywords | Field | DocType |
Deep learning,Random access memory,Integrated circuits,Optimization,CMOS integrated circuits | Efficient energy use,Computer science,In-Memory Processing,Parallel computing,Electronic engineering,Robustness (computer science),CMOS,Integrated circuit design,Artificial neural network,Electronic circuit,Resistive random-access memory | Journal |
Volume | Issue | ISSN |
39 | 6 | 0272-1732 |
Citations | PageRank | References |
5 | 0.53 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shihui Yin | 1 | 71 | 10.03 |
| Jae-sun Seo | 2 | 536 | 56.32 |
| Yulhwa Kim | 3 | 13 | 4.80 |
| Xu Han | 4 | 5 | 0.53 |
| Hugh J. Barnaby | 5 | 26 | 7.17 |
| Shimeng Yu | 6 | 490 | 56.22 |
| Yandong Luo | 7 | 17 | 2.82 |
| Wangxin He | 8 | 5 | 0.53 |
| Xiaoyu Sun | 9 | 95 | 16.54 |
| Jae-Joon Kim | 10 | 275 | 37.46 |