Name
Abstract | ||
|---|---|---|
In this paper, we present the realization of neural rate coding with probabilistic spike trains by using two back-to-back magnetic tunnel junctions (MTJs) for the bio-inspired computing applications. By exploiting the intrinsic stochastic switching of the MTJ device between two different resistance states, an analog stimulus can be converted into a probabilistic spike train and its spike rate can be modulated by the switching probability, which depends on the magnitude of the stimulus. To implement such conversion, we propose a hybrid CMOS/MTJ circuit. By using a physics-based MTJ compact model and a commercial CMOS 40nm design kit, its functionality has been validated. Additionally, we also investigate the relationship between switching probability and stimulus by Monte Carlo simulations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/NVMTS.2015.7457499 | 2015 15th Non-Volatile Memory Technology Symposium (NVMTS) |
Keywords | Field | DocType |
neural rate coding,probabilistic spike train,bio-inspired computing,magnetic tunnel junction (MTJ),stochatic switching,Monte Carlo simulations | Monte Carlo method,Spike train,Computer science,Neural coding,Stochastic process,Electronic engineering,CMOS,Probabilistic logic,Frequency modulation,Tunnel magnetoresistance | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| De-ming Zhang | 1 | 19 | 4.81 |
| Lang Zeng | 2 | 18 | 4.67 |
| Fanghui Gong | 3 | 0 | 0.34 |
| Tianqi Gao | 4 | 5 | 3.56 |
| Shaolong Gao | 5 | 0 | 0.34 |
| Youguang Zhang | 6 | 21 | 7.75 |
| Weisheng Zhao | 7 | 730 | 105.43 |