Name
Title | ||
|---|---|---|
An Energy-Efficient Deep Belief Network Processor Based on Heterogeneous Multi-Core Architecture With Transposable Memory and On-Chip Learning |
Abstract | ||
|---|---|---|
With the growing interest of edge computing in the Internet of Things (IoT), Deep Neural Network (DNN) hardware processors/accelerators face challenges of low energy consumption, low latency, and data privacy issues. This paper proposes an energy-efficient processor design based on Deep Belief Network (DBN), which is one of the most suitable DNN models for on- chip learning. In this study, a thoro... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JETCAS.2021.3114396 | IEEE Journal on Emerging and Selected Topics in Circuits and Systems |
Keywords | DocType | Volume |
Neurons,Energy efficiency,Computational modeling,Unsupervised learning,System-on-chip,Integrated circuit modeling,Computer architecture | Journal | 11 |
Issue | ISSN | Citations |
4 | 2156-3357 | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiajun Wu | 1 | 1 | 1.36 |
| Xuan Huang | 2 | 0 | 0.34 |
| Le Yang | 3 | 9 | 2.48 |
| Jipeng Wang | 4 | 0 | 0.68 |
| Bingqiang Liu | 5 | 0 | 0.34 |
| Ziyuan Wen | 6 | 0 | 0.34 |
| Juhui Li | 7 | 0 | 0.34 |
| Guoyi Yu | 8 | 0 | 1.69 |
| Kwen-Siong Chong | 9 | 1 | 2.04 |
| Chao Wang | 10 | 0 | 0.68 |