Name
Title | ||
|---|---|---|
Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator |
Abstract | ||
|---|---|---|
Resistive Random-Access-Memory (ReRAM) crossbar is one of the most promising neural network accelerators, thanks to its in-memory and in-situ analog computing abilities for Matrix Multiplication-and-Accumulations (MACs). Nevertheless, the number of rows and columns of ReRAM cells for concurrent execution of MACs is constrained, resulting in limited in-memory computing throughput. Moreover, it is c... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCAD51958.2021.9643569 | 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD) |
Keywords | DocType | ISSN |
Quantization (signal),Computational modeling,Neural networks,Computer architecture,Performance gain,Throughput,Inference algorithms | Conference | 1933-7760 |
ISBN | Citations | PageRank |
978-1-6654-4507-8 | 0 | 0.34 |
References | Authors | |
18 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fangxin Liu | 1 | 1 | 1.03 |
| wenbo zhao | 2 | 25 | 6.07 |
| Zhezhi He | 3 | 136 | 25.37 |
| Zongwu Wang | 4 | 0 | 4.06 |
| Yilong Zhao | 5 | 2 | 3.08 |
| Yongbiao Chen | 6 | 0 | 0.68 |
| Li Jiang | 7 | 286 | 31.86 |