Magnetic Skyrmion Neurons with Homeostasis for Spiking Neural Networks
Source abstract
Abstract
Recent advancements in spiking neural networks (SNNs) have drawn inspiration from the human brain's distinctive capabilities, leading to significant impacts on various aspects of our lives and scientific endeavors. The development of hardware-based Spiking Neural Networks (H-SNNs) is a crucial aspect of the progress in neuromorphic computing, particularly in the context of on-chip implementations. H-SNNs involve the hardware realization of specific functionalities used in SNN algorithms, such as the leaky integrate-and-fire (LIF). One crucial functionality among these is neuronal homeostasis. However, although its importance in learning processes has been established, its hardware implementation at a single device remains unexplored until now. Here, we introduce a novel skyrmion-based spiking neuron device, which achieves hardware-level implementation of neuronal homeostasis. We have successfully demonstrated the modulation of a neuron's threshold value through gate voltage application, a major step forward in demonstrating the feasibility of neuronal homeostasis in a singular hardware unit. We believe that our findings have significant implications, not only for the development of advanced Spiking Neural Network (SNN)-based on-chip devices but also for the potential application of skyrmions in electronic devices.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Magnetic properties of thin films · Neural Networks and Reservoir Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Quantum Technology Institute, Korea Research Institute of Standards and Science
Department of Physics
Pusan National University
Center for Neuromorphic Engineering
Korea Institute of Science and Technology
Division of Quantum Phases and Devices, Department of Physics
Konkuk University
Funding
Ministry of Education · National Research Council of Science and Technology · National Research Foundation of Korea · Ministry of Trade, Industry and Energy · Korea Institute of Science and Technology
References (44)
Show source-provided references
1. 10.1038/nature1453910.1038/nature14539 ↗
2. 10.1109/MSP.2012.220559710.1109/MSP.2012.2205597 ↗
3. Simonyan, K.; Zisserman, A. In Very deep convolutional networks for large-scale image recognition , Proceedings of the 3rd International Conference on Learning Representations; ICLR, 2014.
4. 10.1142/S012906570900200210.1142/S0129065709002002 ↗
5. 10.1006/jtbi.1998.078210.1006/jtbi.1998.0782 ↗
6. 10.1166/jnn.2019.1702510.1166/jnn.2019.17025 ↗
7. Wade, J. J.; McDaid, L. J.; Santos, J. A.; Sayers, H. M. In SWAT: An unsupervised SNN training algorithm for classification problems ,2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence); IEEE, 2008; pp 2648–2655.10.1109/IJCNN.2008.4634169 ↗
8. 10.1016/j.sse.2019.10774110.1016/j.sse.2019.107741 ↗
9. 10.3389/fncom.2021.64612510.3389/fncom.2021.646125 ↗
10. 10.1109/TNANO.2013.225099510.1109/TNANO.2013.2250995 ↗
11. Habenschuss, S.; Bill, J.; Nessler, B. In Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints , Advances in Neural Information Processing Systems; NeurIPS: Nevada, USA, 2012; pp ; pp 773–781.
12. 10.3389/fncom.2015.0009910.3389/fncom.2015.00099 ↗
13. 10.1109/LED.2024.341670410.1109/LED.2024.3416704 ↗
14. 10.3389/fnins.2021.70905310.3389/fnins.2021.709053 ↗
15. 10.1038/s41586-022-04992-810.1038/s41586-022-04992-8 ↗
16. 10.1038/s41928-021-00627-410.1038/s41928-021-00627-4 ↗
17. Hassan, N.; Brigner, W. H.; Hu, X.; Akinola, O. G.; Bennett, C. H.; Marinella, M. J.; Garcia-Sanchez, F.; Incorvia, J. A. C.; Friedman, J. S. In CMOS-Free Magnetic Domain Wall Leaky Integrate-and-Fire Neurons with Intrinsic Lateral Inhibition , 2020 IEEE International Symposium on Circuits and Systems (ISCAS); IEEE, 2020; pp 1–5.10.1109/ISCAS45731.2020.9180510 ↗
18. 10.1109/TED.2022.315950810.1109/TED.2022.3159508 ↗
19. 10.1039/C7NR09722K10.1039/C7NR09722K ↗
20. 10.1038/s41598-022-12555-010.1038/s41598-022-12555-0 ↗
21. 10.1088/2634-4386/adad0e10.1088/2634-4386/adad0e ↗
22. 10.1109/TED.2024.344216110.1109/TED.2024.3442161 ↗
23. 10.1002/adma.20190063610.1002/adma.201900636 ↗
24. 10.1021/acs.nanolett.2c0240910.1021/acs.nanolett.2c02409 ↗
25. 10.1038/s41467-023-36728-110.1038/s41467-023-36728-1 ↗
26. 10.1002/aelm.20250009110.1002/aelm.202500091 ↗
27. Sekh, B.; Kumar, D.; Rahaman, H.; Maddu, R.; Chan, J.; Mah, W. L. W.; Piramanayagam, S. N. Leaky-Integrate-Fire Neuron via Synthetic Antiferromagnetic Coupling and Spin-Orbit Torque. 2024, arXiv:2408.08525. arXiv.org e-Print archive, https://arxiv.org/abs/2408.08525.
28. 10.1038/nmat280410.1038/nmat2804 ↗
29. 10.1103/PhysRevB.84.05440110.1103/PhysRevB.84.054401 ↗
30. 10.1021/acs.nanolett.2c0226810.1021/acs.nanolett.2c02268 ↗
31. 10.1063/5.015377710.1063/5.0153777 ↗
32. 10.1038/s41598-018-29397-410.1038/s41598-018-29397-4 ↗
33. 10.1002/adma.20210440610.1002/adma.202104406 ↗
34. 10.1063/1.499136010.1063/1.4991360 ↗
35. 10.1016/j.jmmm.2021.16838110.1016/j.jmmm.2021.168381 ↗
36. 10.1002/adma.20220888110.1002/adma.202208881 ↗
37. 10.1103/PhysRevB.82.18441610.1103/PhysRevB.82.184416 ↗
38. 10.1103/PhysRevB.96.14440810.1103/PhysRevB.96.144408 ↗
39. 10.1126/science.aaa144210.1126/science.aaa1442 ↗
40. 10.1038/srep0764310.1038/srep07643 ↗
41. 10.1063/1.489918610.1063/1.4899186 ↗
42. 10.1038/nphys388310.1038/nphys3883 ↗
43. 10.1038/23610.1038/236 ↗
44. 10.1002/adma.20220327510.1002/adma.202203275 ↗