Hardware in Loop Learning with Spin Stochastic Neurons
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913277206855680 |
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| author | Islam, A N M Nafiul Yang, Kezhou Shukla, Amit K. Khanal, Pravin Zhou, Bowei Wang, Wei-Gang Sengupta, Abhronil |
| author_facet | Islam, A N M Nafiul Yang, Kezhou Shukla, Amit K. Khanal, Pravin Zhou, Bowei Wang, Wei-Gang Sengupta, Abhronil |
| contents | Despite the promise of superior efficiency and scalability, real-world deployment of emerging nanoelectronic platforms for brain-inspired computing have been limited thus far, primarily because of inter-device variations and intrinsic non-idealities. In this work, we demonstrate mitigating these issues by performing learning directly on practical devices through a hardware-in-loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin-orbit torque heterostructures. We characterize the probabilistic switching and device-to-device variability of our fabricated devices of various sizes to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network-level performance. The efficacy of the hardware-in-loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large-scale implementations of neuromorphic hardware and realization of truly autonomous edge-intelligent devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03235 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hardware in Loop Learning with Spin Stochastic Neurons Islam, A N M Nafiul Yang, Kezhou Shukla, Amit K. Khanal, Pravin Zhou, Bowei Wang, Wei-Gang Sengupta, Abhronil Emerging Technologies Despite the promise of superior efficiency and scalability, real-world deployment of emerging nanoelectronic platforms for brain-inspired computing have been limited thus far, primarily because of inter-device variations and intrinsic non-idealities. In this work, we demonstrate mitigating these issues by performing learning directly on practical devices through a hardware-in-loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin-orbit torque heterostructures. We characterize the probabilistic switching and device-to-device variability of our fabricated devices of various sizes to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network-level performance. The efficacy of the hardware-in-loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large-scale implementations of neuromorphic hardware and realization of truly autonomous edge-intelligent devices. |
| title | Hardware in Loop Learning with Spin Stochastic Neurons |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2305.03235 |