完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Wu, Ming-Hung | en_US |
dc.contributor.author | Hong, Ming-Chun | en_US |
dc.contributor.author | Chang, Chih-Cheng | en_US |
dc.contributor.author | Sahu, Paritosh | en_US |
dc.contributor.author | Wei, Jeng-Hua | en_US |
dc.contributor.author | Lee, Heng-Yuan | en_US |
dc.contributor.author | Sheu, Shyh-Shyuan | en_US |
dc.contributor.author | Hou, Tuo-Hung | en_US |
dc.date.accessioned | 2020-10-05T02:01:30Z | - |
dc.date.available | 2020-10-05T02:01:30Z | - |
dc.date.issued | 2019-01-01 | en_US |
dc.identifier.isbn | 978-4-86348-719-2; 978-4-86348-717-8 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/155280 | - |
dc.description.abstract | This work reports the complete framework from device to architecture for deep learning acceleration in an all-spin artificial neural network (ANN) built by highly manufacturable STT-MRAM technology. The most compact analog integrate-and-fire neuron reported to date is developed based on the back-hopping oscillation in magnetic tunnel junctions. This novel device is unique because it performs numerous essential neural functions simultaneously, including current integration, voltage spike generation, state reset, and 4-bit precision. The device itself is also a stochastic binary synapse, and thus eases the implementation of the compact all-spin ANN with high accuracy for online training. | en_US |
dc.language.iso | en_US | en_US |
dc.title | Extremely Compact Integrate-and-Fire STT-MRAM Neuron: A Pathway toward All-Spin Artificial Deep Neural Network | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2019 SYMPOSIUM ON VLSI TECHNOLOGY | en_US |
dc.citation.spage | 0 | en_US |
dc.citation.epage | 0 | en_US |
dc.contributor.department | 電子工程學系及電子研究所 | zh_TW |
dc.contributor.department | Department of Electronics Engineering and Institute of Electronics | en_US |
dc.identifier.wosnumber | WOS:000555822600080 | en_US |
dc.citation.woscount | 2 | en_US |
顯示於類別: | 會議論文 |