Name
Title | ||
|---|---|---|
DNPU: An Energy-Efficient Deep-Learning Processor with Heterogeneous Multi-Core Architecture. |
Abstract | ||
|---|---|---|
An energy-efficient deep-learning processor called DNPU is proposed for the embedded processing of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in mobile platforms. DNPU uses a heterogeneous multi-core architecture to maximize energy efficiency in both CNNs and RNNs. In each core, a memory architecture, data paths, and processing elements are optimized depending on the... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MM.2018.053631145 | IEEE Micro |
Keywords | Field | DocType |
Quantization (signal),Convolution,System-on-chip,Energy efficiency,Memory management | System on a chip,Computer science,Convolutional neural network,Parallel computing,Recurrent neural network,Memory management,Artificial intelligence,Deep learning,Artificial neural network,Quantization (signal processing),Memory architecture | Journal |
Volume | Issue | ISSN |
38 | 5 | 0272-1732 |
Citations | PageRank | References |
3 | 0.38 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongjoo Shin | 1 | 67 | 11.02 |
| Jinmook Lee | 2 | 65 | 9.34 |
| Jinsu Lee | 3 | 23 | 7.89 |
| Juhyoung Lee | 4 | 3 | 1.40 |
| Hoi-Jun Yoo | 5 | 1574 | 226.79 |