Name
Title | ||
|---|---|---|
Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) have led to remarkable progress in a number of key pattern recognition tasks, such as visual scene understanding and speech recognition, that potentially enable numerous applications. Consequently, there is a significant need to deploy trained CNNs to resource-constrained embedded systems. Inference using pretrained modern deep CNNs, however, requires significa... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2018.2808319 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Energy dissipation,Neural networks,Dynamic range,Embedded systems,Training,Quantization (signal),Learning systems | Pattern recognition,Computer science,Convolutional neural network,Inference,Caffè,Software,Artificial intelligence,Fixed point,Artificial neural network,Machine learning,Empirical research,Computation | Journal |
Volume | Issue | ISSN |
29 | 11 | 2162-237X |
Citations | PageRank | References |
23 | 1.22 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philipp Gysel | 1 | 72 | 4.17 |
| Jon J. Pimentel | 2 | 58 | 4.50 |
| Mohammad Motamedi | 3 | 132 | 9.53 |
| Soheil Ghiasi | 4 | 352 | 34.74 |