Name
Title | ||
|---|---|---|
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network |
Abstract | ||
|---|---|---|
Resistive Random-Access-Memory (ReRAM) cross-bar is a promising technique for deep neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing abilities for Vector-Matrix Multiplication-and-Accumulations (VMMs). However, it is challenging for crossbar architecture to exploit the sparsity in DNNs. It inevitably causes complex and costly control to exploit fine-grained sp... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCD53106.2021.00072 | 2021 IEEE 39th International Conference on Computer Design (ICCD) |
Keywords | DocType | ISSN |
Deep learning,Quantization (signal),Virtual machine monitors,Microprocessors,Conferences,Computer architecture,Artificial neural networks | Conference | 1063-6404 |
ISBN | Citations | PageRank |
978-1-6654-3219-1 | 0 | 0.34 |
References | Authors | |
15 | 9 | |
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 |
| Yang, Tao | 6 | 3 | 6.50 |
| Jingnai Feng | 7 | 0 | 0.34 |
| Xiaoyao Liang | 8 | 585 | 45.81 |
| Li Jiang | 9 | 286 | 31.86 |