Name
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) are a vital approach in machine learning. However, their high complexity and energy consumption make them challenging to embed in mobile applications at the edge requiring real-time processes such as smart phones. In order to meet the real-time constraint of edge devices, recently proposed custom hardware CNN accelerators have exploited parallel processing elem... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TC.2019.2941875 | IEEE Transactions on Computers |
Keywords | Field | DocType |
Bandwidth,Performance evaluation,Runtime,Computational modeling,Three-dimensional displays,Edge computing,Parallel processing | Edge computing,Efficient energy use,Computer science,Convolutional neural network,Parallel computing,Edge device,Dataflow,Bandwidth (signal processing),Throughput,Energy consumption,Computer engineering | Journal |
Volume | Issue | ISSN |
69 | 1 | 0018-9340 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arash Ardakani | 1 | 33 | 8.42 |
| Carlo Condo | 2 | 132 | 21.40 |
| Warren J. Gross | 3 | 1106 | 113.38 |