Name
Abstract | ||
|---|---|---|
Recurrent Neural Network (RNN) is a key technology for sequential applications which require efficient and realtime implementations. Despite its popularity, efficient acceleration for RNN inference is challenging due to its recurrent nature and data dependencies. This paper proposes a multi-threaded neural processing unit (NPU) for RNN/LSTM inferences to increase processing abilities and quality o... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICFPT51103.2020.00012 | 2020 International Conference on Field-Programmable Technology (ICFPT) |
Keywords | DocType | ISBN |
Recurrent neural networks,Instruction sets,Computer architecture,Quality of service,Parallel processing,Throughput,Hardware | Conference | 978-1-6654-2302-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Que | 1 | 26 | 9.81 |
| Hiroki Nakahara | 2 | 155 | 37.34 |
| Hongxiang Fan | 3 | 23 | 7.57 |
| Jiuxi Meng | 4 | 3 | 1.82 |
| Kuen Hung Tsoi | 5 | 0 | 1.69 |
| Xinyu Niu | 6 | 135 | 23.16 |
| Eriko Nurvitadhi | 7 | 0 | 0.34 |
| Wayne Luk | 8 | 15 | 10.38 |