Name
Abstract | ||
|---|---|---|
Processing In-Memory (PIM) has shown a great potential to accelerate inference tasks of Convolutional Neural Network (CNN). However, existing PIM architectures do not support high precision computation, e.g., in floating point precision, which is essential for training accurate CNN models. In addition, most of the existing PIM approaches require analog/mixed-signal circuits, which do not scale, ex... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SOCC49529.2020.9524776 | 2020 IEEE 33rd International System-on-Chip Conference (SOCC) |
DocType | ISBN | Citations |
Conference | 978-1-7281-8746-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohsen Imani | 1 | 341 | 48.13 |
| Saransh Gupta | 2 | 101 | 11.58 |
| Yeseong Kim | 3 | 72 | 8.35 |
| Tajana Simunic | 4 | 3198 | 266.23 |