Name
Abstract | ||
|---|---|---|
The computing wall and data movement challenges of deep neural networks (DNNs) have exposed the limitations of conventional CMOS-based DNN accelerators. Furthermore, the deep structure and large model size will make DNNs prohibitive to embedded systems and IoT devices, where low power consumption is required. To address these challenges, spin-orbit torque magnetic random-access memory (SOT-MRAM) a... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SOCC49529.2020.9524757 | 2020 IEEE 33rd International System-on-Chip Conference (SOCC) |
Keywords | DocType | ISBN |
Training,Power demand,Quantization (signal),Embedded systems,Torque,System performance,Writing | Conference | 978-1-7281-8746-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geng Yuan | 1 | 7 | 3.56 |
| Xiaolong Ma | 2 | 22 | 5.90 |
| Sheng Lin | 3 | 139 | 14.39 |
| Zhengang Li | 4 | 15 | 7.27 |
| Jieren Deng | 5 | 0 | 0.68 |
| Caiwen Ding | 6 | 142 | 26.52 |