Name
Title | ||
|---|---|---|
High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator |
Abstract | ||
|---|---|---|
Deep convolution Neural Network (DCNN) has been widely used in computer vision tasks. However, for edge device, even then inference has too large computational complexity and data access amount. Due to the mentioned shortcomings, the inference latency of state-of-the-art models are still impractical for real-world applications. In this paper, we proposed a high utilization energy-aware real-time i... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCAS51556.2021.9401526 | 2021 IEEE International Symposium on Circuits and Systems (ISCAS) |
Keywords | DocType | ISSN |
Convolution,Computational modeling,Random access memory,Real-time systems,Hardware,Energy efficiency,Convolutional neural networks | Conference | 0271-4302 |
ISBN | Citations | PageRank |
978-1-7281-9201-7 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuan-Ting Lin | 1 | 4 | 1.83 |
| Ching-Te Chiu | 2 | 304 | 38.60 |
| Jheng-Yi Chang | 3 | 0 | 0.34 |
| Shan-Chien Hsiao | 4 | 0 | 0.34 |