Name
Abstract | ||
|---|---|---|
Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on embedded platforms is vital to enable the development of intelligent applications. This paper summarises our recent work on the optimised mapping of DNNs on embedded settings. By covering such diverse topics as DNN-to-accelerator toolflows, high-throughput cascaded classifiers and domain-specific model design, the presented set of works aim to enable the deployment of sophisticated deep learning models on cutting-edge mobile and embedded systems. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Computer architecture,Software deployment,Computer science,Internet of Things,Artificial intelligence,Deep learning,Machine learning,Deep neural networks,Applications of artificial intelligence |
DocType | Volume | Citations |
Journal | abs/1806.08616 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stylianos I. Venieris | 1 | 106 | 12.98 |
| Alexandros Kouris | 2 | 0 | 0.68 |
| Christos Savvas Bouganis | 3 | 400 | 49.04 |