Name
Abstract | ||
|---|---|---|
Deep learning (DL) has gained unprecedented success in many real-world applications. However, DL poses difficulties for efficient hardware implementation due to the needs of a complex gradient-based learning algorithm and the required high memory bandwidth for synaptic weight storage, especially in today’s data-intensive environment. Computing-in-memory (CIM) strategies have emerged as an alternat... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCSI.2021.3071956 | IEEE Transactions on Circuits and Systems I: Regular Papers |
Keywords | DocType | Volume |
Reservoirs,Training,Hardware,Feature extraction,Memory management,Biological neural networks,Deep learning | Journal | 68 |
Issue | ISSN | Citations |
7 | 1549-8328 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kangjun Bai | 1 | 11 | 5.28 |
| Lingjia Liu | 2 | 799 | 92.58 |
| Yi Yang | 3 | 92 | 9.96 |