Name
Title | ||
|---|---|---|
Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges |
Abstract | ||
|---|---|---|
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unmatchable performance in several applications, such as image processing, computer vision, and natural language processing. However, as DNNs grow in their complexity, their associated energy consumption becomes a challenging problem. Such challenge heightens for edge computing, where the computing devices are resource-constrained while operating on limited energy budget. Therefore, specialized optimizations for deep learning have to be performed at both software and hardware levels. In this paper, we comprehensively survey the current trends of such optimizations and discuss key open research mid-term and long-term challenges. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISVLSI.2019.00105 | 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) |
Keywords | Field | DocType |
pre-processing,pruning,quantization,DNN,accelerator,hardware,software,performance,energy efficiency,low power,deep learning,neural networks,edge computing,IoT | Edge computing,Cross layer,Open research,Computer architecture,Computer science,Image processing,Software,Artificial intelligence,Deep learning,Energy consumption,Market research | Conference |
ISSN | ISBN | Citations |
2159-3469 | 978-1-7281-3392-8 | 10 |
PageRank | References | Authors |
0.56 | 14 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Marchisio | 1 | 32 | 8.58 |
| Muhammad Abdullah Hanif | 2 | 71 | 18.12 |
| Faiq Khalid | 3 | 17 | 1.39 |
| George Plastiras | 4 | 17 | 2.30 |
| Christos Kyrkou | 5 | 102 | 14.05 |
| Theocharis Theocharides | 6 | 205 | 26.83 |
| Muhammad Shafique | 7 | 1945 | 157.67 |