Name
Abstract | ||
|---|---|---|
To efficiently deploy machine learning applications to the edge, compute-in-memory (CIM) based hardware accelerator is a promising solution with improved throughput and energy efficiency. Instant-on inference is further enabled by emerging non-volatile memory technologies such as resistive random access memory (RRAM). This paper reviews the recent progresses of the RRAM based CIM accelerator desig... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCSI.2021.3072200 | IEEE Transactions on Circuits and Systems I: Regular Papers |
Keywords | DocType | Volume |
Computer architecture,Training,Resistance,Microprocessors,Random access memory,Arrays,Reliability | Journal | 68 |
Issue | ISSN | Citations |
7 | 1549-8328 | 9 |
PageRank | References | Authors |
0.55 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shimeng Yu | 1 | 490 | 56.22 |
| Wonbo Shim | 2 | 9 | 0.89 |
| Xiaochen Peng | 3 | 61 | 12.17 |
| Yandong Luo | 4 | 17 | 2.82 |