Dual Precision Deep Neural Network
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2020
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913346768338944 |
|---|---|
| author | Park, Jae Hyun Choi, Ji Sub Ko, Jong Hwan |
| author_facet | Park, Jae Hyun Choi, Ji Sub Ko, Jong Hwan |
| contents | On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference. In this paper, we propose dual-precision DNN that includes two different precision modes in a single model, thereby supporting an on-line precision switch without re-training. The proposed two-phase training process optimizes both low- and high-precision modes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2009_02191 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Dual Precision Deep Neural Network Park, Jae Hyun Choi, Ji Sub Ko, Jong Hwan Machine Learning Computer Vision and Pattern Recognition On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference. In this paper, we propose dual-precision DNN that includes two different precision modes in a single model, thereby supporting an on-line precision switch without re-training. The proposed two-phase training process optimizes both low- and high-precision modes. |
| title | Dual Precision Deep Neural Network |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2009.02191 |