Name
Title | ||
|---|---|---|
Energy-efficient neuromorphic computation based on compound spin synapse with stochastic learning |
Abstract | ||
|---|---|---|
Recently, magnetic tunnel junction with in-plane magnetization (i-MTJ) has been exploited to behave as a binary stochastic synapse. However, it suffers from its limited level of synaptic weight, resulting in an inaccurate learning. In this work, a compound synapse that employs multiple perpendicular MTJs (p-MTJs) in series is proposed. It possesses an analog-like synaptic weight under weak programming conditions, which leads to a stochastic learning rule and low power consumption per synaptic event. By performing system-level simulations on the MNIST database, it has been demonstrated that such compound spin synapses can realize stochastic neuromorphic computation with high accuracy and low energy consumption. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISCAS.2015.7168939 | International Symposium on Circuits and Systems |
Keywords | Field | DocType |
neuromorphic computation, p-MTJ, binary synaptic device, STT, stochastic learning rule, winner-takes-all network | MNIST database,Computer science,Efficient energy use,Neuromorphic engineering,Electronic engineering,Learning rule,Tunnel magnetoresistance,Synaptic weight,Computation,Binary number | Conference |
ISSN | Citations | PageRank |
0271-4302 | 4 | 0.55 |
References | Authors | |
7 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| De-ming Zhang | 1 | 19 | 4.81 |
| Lang Zeng | 2 | 18 | 4.67 |
| Yuanzhuo Qu | 3 | 4 | 0.55 |
| Youguang Zhang | 4 | 21 | 7.75 |
| Mengxing Wang | 5 | 4 | 0.55 |
| Weisheng Zhao | 6 | 730 | 105.43 |
| Tianqi Tang | 7 | 342 | 19.66 |
| Yu Wang | 8 | 2279 | 211.60 |