標題: Extremely Compact Integrate-and-Fire STT-MRAM Neuron: A Pathway toward All-Spin Artificial Deep Neural Network
作者: Wu, Ming-Hung
Hong, Ming-Chun
Chang, Chih-Cheng
Sahu, Paritosh
Wei, Jeng-Hua
Lee, Heng-Yuan
Sheu, Shyh-Shyuan
Hou, Tuo-Hung
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
公開日期: 1-Jan-2019
摘要: 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.
URI: http://hdl.handle.net/11536/155280
ISBN: 978-4-86348-719-2; 978-4-86348-717-8
期刊: 2019 SYMPOSIUM ON VLSI TECHNOLOGY
起始頁: 0
結束頁: 0
Appears in Collections:Conferences Paper