Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929692986048512 |
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| author | Rahman, Rahnuma Bandyopadhyay, Supriyo |
| author_facet | Rahman, Rahnuma Bandyopadhyay, Supriyo |
| contents | Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties -- "binary" where the neuronal statevaries randomly between two values of -1, +1 and "analog" where the neuronal state can randomly assume any value between -1 and +1. Both have their uses in neuromorphic computing and both can be implemented with low- or zero-energy-barrier nanomagnets whose random magnetization orientations in the presence of thermal noise encode the binary or analog state variables. In between these two classes is n-ary stochastic neurons, mainly ternary stochastic neurons (TSN) whose state randomly assumes one of three values (-1, 0, +1), which have proved to be efficient in pattern classification tasks such as recognizing handwritten digits from the MNIST data set or patterns from the CIFAR-10 data set. Here, we show how to implement a TSN with a zero-energy-barrier (shape isotropic) magnetostrictive nanomagnet subjected to uniaxial strain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04246 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet Rahman, Rahnuma Bandyopadhyay, Supriyo Mesoscale and Nanoscale Physics Signal Processing Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties -- "binary" where the neuronal statevaries randomly between two values of -1, +1 and "analog" where the neuronal state can randomly assume any value between -1 and +1. Both have their uses in neuromorphic computing and both can be implemented with low- or zero-energy-barrier nanomagnets whose random magnetization orientations in the presence of thermal noise encode the binary or analog state variables. In between these two classes is n-ary stochastic neurons, mainly ternary stochastic neurons (TSN) whose state randomly assumes one of three values (-1, 0, +1), which have proved to be efficient in pattern classification tasks such as recognizing handwritten digits from the MNIST data set or patterns from the CIFAR-10 data set. Here, we show how to implement a TSN with a zero-energy-barrier (shape isotropic) magnetostrictive nanomagnet subjected to uniaxial strain. |
| title | Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet |
| topic | Mesoscale and Nanoscale Physics Signal Processing |
| url | https://arxiv.org/abs/2412.04246 |