Abstract
Spintronic devices, especially the magnetic tunnel junction and magnetic domain wall-based devices, hold significant promise for applications in energy-efficient data storage and Unconventional computing architectures. We present a novel multilayer spintronic neuromorphic device based on spin-orbit torque-driven domain wall dynamics. The typical leaky integrate and fire LIF neuron-like characteristics are realized using the combination of SOT and demagnetization energy effects. The device characteristics are modelled as the modified LIF model. We test the spiking neuron model for the classification of the MNIST dataset by emulating a 3-layer spiking neural network SNN -based on the DW-MTJ LIF neuron model. The network achieves classification accuracy above 96% thus the proposed device can be integrated with the CMOS for energy efficient neuromorphic computing.
| Original language | English (US) |
|---|---|
| Title of host publication | 2024 IEEE 24th International Conference on Nanotechnology, NANO 2024 |
| Publisher | IEEE Computer Society |
| Pages | 146-149 |
| Number of pages | 4 |
| ISBN (Electronic) | 9798350386240 |
| DOIs | |
| State | Published - 2024 |
| Event | 24th IEEE International Conference on Nanotechnology, NANO 2024 - Gijon, Spain Duration: Jul 8 2024 → Jul 11 2024 |
Publication series
| Name | Proceedings of the IEEE Conference on Nanotechnology |
|---|---|
| ISSN (Print) | 1944-9399 |
| ISSN (Electronic) | 1944-9380 |
Conference
| Conference | 24th IEEE International Conference on Nanotechnology, NANO 2024 |
|---|---|
| Country/Territory | Spain |
| City | Gijon |
| Period | 07/8/24 → 07/11/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Domain wall devices
- Leaky-integrate and fire neurons
- Magnetic tunnel junction
- Neuromorphic Computing
- Spiking neural networks (SNN)
- Spintronics
ASJC Scopus subject areas
- Bioengineering
- Electrical and Electronic Engineering
- Materials Chemistry
- Condensed Matter Physics