Neuromorphic weighted sums with magnetic skyrmions
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866916830411489280 |
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| author | Gomes, Tristan da Câmara Santa Clara Sassi, Yanis Sanz-Hernández, Dédalo Krishnia, Sachin Collin, Sophie Martin, Marie-Blandine Seneor, Pierre Cros, Vincent Grollier, Julie Reyren, Nicolas |
| author_facet | Gomes, Tristan da Câmara Santa Clara Sassi, Yanis Sanz-Hernández, Dédalo Krishnia, Sachin Collin, Sophie Martin, Marie-Blandine Seneor, Pierre Cros, Vincent Grollier, Julie Reyren, Nicolas |
| contents | Integrating magnetic skyrmions into neuromorphic computing could help improve hardware efficiency and computational power. However, developing a scalable implementation of the weighted sum of neuron signals - a core operation in neural networks - has remained a challenge. Here, we show that weighted sum operations can be performed in a compact, biologically-inspired manner by using the non-volatile and particle-like characteristics of magnetic skyrmions that make them easily countable and summable. The skyrmions are electrically generated in numbers proportional to an input with an efficiency given by a non-volatile weight. The chiral particles are then directed using localized current injections to a location where their presence is quantified through non-perturbative electrical measurements. Our experimental demonstration, which currently has two inputs, can be scaled to accommodate multiple inputs and outputs using a crossbar array design, potentially nearing the energy efficiency observed in biological systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_16909 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Neuromorphic weighted sums with magnetic skyrmions Gomes, Tristan da Câmara Santa Clara Sassi, Yanis Sanz-Hernández, Dédalo Krishnia, Sachin Collin, Sophie Martin, Marie-Blandine Seneor, Pierre Cros, Vincent Grollier, Julie Reyren, Nicolas Emerging Technologies Materials Science Integrating magnetic skyrmions into neuromorphic computing could help improve hardware efficiency and computational power. However, developing a scalable implementation of the weighted sum of neuron signals - a core operation in neural networks - has remained a challenge. Here, we show that weighted sum operations can be performed in a compact, biologically-inspired manner by using the non-volatile and particle-like characteristics of magnetic skyrmions that make them easily countable and summable. The skyrmions are electrically generated in numbers proportional to an input with an efficiency given by a non-volatile weight. The chiral particles are then directed using localized current injections to a location where their presence is quantified through non-perturbative electrical measurements. Our experimental demonstration, which currently has two inputs, can be scaled to accommodate multiple inputs and outputs using a crossbar array design, potentially nearing the energy efficiency observed in biological systems. |
| title | Neuromorphic weighted sums with magnetic skyrmions |
| topic | Emerging Technologies Materials Science |
| url | https://arxiv.org/abs/2310.16909 |