Name
Title | ||
|---|---|---|
Receptive-Field and Switch-Matrices Based ReRAM Accelerator with Low Digital-Analog Conversion for CNNs |
Abstract | ||
|---|---|---|
Process-in-Memory (PIM) based accelerator becomes one of the best solutions for the execution of convolution neural networks (CNN). Resistive random access memory (ReRAM) is a classic type of non-volatile random-access memory, which is very suitable for implementing PIM architectures. However, existing ReRAM-based accelerators mainly consider to improve the calculation efficiency, but ignore the f... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/DATE51398.2021.9474181 | 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) |
Keywords | DocType | ISBN |
Energy consumption,Convolution,Nonvolatile memory,Energy resources,Digital-analog conversion,Resistive RAM,Neural networks | Conference | 978-3-9819263-5-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingxun Fu | 1 | 20 | 4.43 |
| Xun Liu | 2 | 0 | 0.68 |
| Jiwu Shu | 3 | 709 | 72.71 |
| Zhirong Shen | 4 | 85 | 18.72 |
| Shiye Zhang | 5 | 0 | 0.68 |
| Jun Wu | 6 | 0 | 0.68 |
| Li Ma | 7 | 0 | 0.34 |