Name
Abstract | ||
|---|---|---|
The Internet of Things (IoT) is rapidly enabling applications in many different fields by embedding itself into the physical world. Many potential IoT devices require some level of machine learning or cognitive capability to be truly effective, but the high computational complexity of cognitive algorithms makes them unsuitable for low-power IoT processors. To understand the design challenges of co... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MM.2017.4241339 | IEEE Micro |
Keywords | Field | DocType |
Drones,Performance evaluation,Real-time systems,Object detection,Graphics processing units | Object detection,Data processing,Architecture,Computer architecture,Convolutional neural network,Simulation,Computer science,Real-time computing,Software,Drone,Computational complexity theory,Cloud computing | Journal |
Volume | Issue | ISSN |
37 | 6 | 0272-1732 |
Citations | PageRank | References |
4 | 0.52 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hasan Genc | 1 | 8 | 1.59 |
| Yazhou Zu | 2 | 40 | 5.20 |
| Ting-Wu Chin | 3 | 26 | 5.66 |
| Matthew Halpern | 4 | 102 | 6.47 |
| Vijay Janapa Reddi | 5 | 2931 | 140.26 |