A binarized spiking neural network based on auto-reset LIF neurons and large signal synapses using STT-MTJs

Haoyan Liu*, Takashi Ohsawa*

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

A binarized spiking neural network using auto-reset leaky integrate-and-fire neurons with a two-transistor and three-magnetic tunnel junction core and large signal synapses with two-transistor and two-magnetic tunnel junctions is designed. The network is applied to a classifier of the MNIST handwritten digit dataset with a 784 × 400 synapse crossbar array. The weights are trained offline using the spike-timing-dependent plasticity learning algorithm and deployed to the spin-transfer torque magnetic tunnel junction (STT-MTJ) resistances in the synapses after being binarized. Its performance is evaluated by HSPICE using the STT-MTJ device model, which takes the stochastic change in the angle between the two magnetic moments in the free and pinned layers into consideration. 75% test accuracy is achieved for 1200 patterns with 1 ns read and 1 ns write operations and 0.23 pJ/SOP energy consumption.

本文言語English
論文番号044501
ジャーナルJapanese journal of applied physics
62
4
DOI
出版ステータスPublished - 2023 4月 1

ASJC Scopus subject areas

  • 工学一般
  • 物理学および天文学一般

フィンガープリント

「A binarized spiking neural network based on auto-reset LIF neurons and large signal synapses using STT-MTJs」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル