| Autor: |
Chenyun Pan, Qiuwen Lou, Michael Niemier, Sharon Hu, Azad Naeemi |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 5, Iss 2, Pp 85-93 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2329-9231 |
| DOI: |
10.1109/JXCDC.2019.2960307 |
| Popis: |
In this article, we perform a uniform benchmarking for the convolutional neural network (CoNN) based on the cellular neural network (CeNN) using a variety of beyond-CMOS technologies. Representative charge-based and spintronic device technologies are implemented to enable energy-efficient CeNN related computations. To alleviate the delay and energy overheads of the fully connected layer, a hybrid spintronic CeNN-based CoNN system is proposed. It is shown that low-power FETs and spintronic devices are promising candidates to implement energy-efficient CoNNs based on CeNNs. Specifically, more than 10× improvement in energy-delay product (EDP) is demonstrated for the systems using spin diffusion-based devices and tunneling FETs compared to their conventional CMOS counterparts. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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