Name
Abstract | ||
|---|---|---|
As Binary Neural Networks (BNNs) started to show promising performance with limited memory and computational cost, various RRAM-based in-memory BNN accelerator designs have been proposed. While a single RRAM cell can represent a binary weight, previous designs had to use two RRAM cells for a weight to enable XNOR operation between a binary weight and a binary activation. In this work, we propose t... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/AICAS51828.2021.9458444 | 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS) |
Keywords | DocType | ISBN |
Microprocessors,Magnetic resonance imaging,Conferences,Neural networks,Accelerator architectures,Phase change random access memory,Energy efficiency | Conference | 978-1-6654-1913-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyunmyung Oh | 1 | 0 | 1.35 |
| HyungJun Kim | 2 | 5 | 7.43 |
| Nameun Kang | 3 | 0 | 0.34 |
| Yulhwa Kim | 4 | 13 | 4.80 |
| Jihoon Park | 5 | 143 | 27.61 |
| Jae-Joon Kim | 6 | 31 | 8.39 |