LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System
Dealing with big data, especially the videos and images, is the biggest challenge of existing Von-Neumann machines while the human brain, benefiting from its massive parallel structure, is capable of processing the images and videos in a fraction of second. The most promising solution, which has bee...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2019.01429/full |
| _version_ | 1818296006306955264 |
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| author | Hooman Farkhani Tim Böhnert Mohammad Tarequzzaman José Diogo Costa Alex Jenkins Ricardo Ferreira Jens Kargaard Madsen Farshad Moradi |
| author_facet | Hooman Farkhani Tim Böhnert Mohammad Tarequzzaman José Diogo Costa Alex Jenkins Ricardo Ferreira Jens Kargaard Madsen Farshad Moradi |
| author_sort | Hooman Farkhani |
| collection | DOAJ |
| description | Dealing with big data, especially the videos and images, is the biggest challenge of existing Von-Neumann machines while the human brain, benefiting from its massive parallel structure, is capable of processing the images and videos in a fraction of second. The most promising solution, which has been recently researched widely, is brain-inspired computers, so-called neuromorphic computing systems (NCS). The NCS overcomes the limitation of the word-at-a-time thinking of conventional computers benefiting from massive parallelism for data processing, similar to the brain. Recently, spintronic-based NCSs have shown the potential of implementation of low-power high-density NCSs, where neurons are implemented using magnetic tunnel junctions (MTJs) or spin torque nano-oscillators (STNOs) and memristors are used to mimic synaptic functionality. Although using STNOs as neuron requires lower energy in comparison to the MTJs, still there is a huge gap between the power consumption of spintronic-based NCSs and the brain due to high bias current needed for starting the oscillation with a detectable output power. In this manuscript, we propose a spintronic-based NCS (196 × 10) proof-of-concept where the power consumption of the NCS is reduced by assisting the STNO oscillation through a microwatt nanosecond laser pulse. The experimental results show the power consumption of the STNOs in the designed NCS is reduced by 55.3% by heating up the STNOs to 100°C. Moreover, the average power consumption of spintronic layer (STNOs and memristor array) is decreased by 54.9% at 100°C compared with room temperature. The total power consumption of the proposed laser assisted STNO-based NCS (LAO-NCS) at 100°C is improved by 40% in comparison to a typical STNO-based NCS at room temperature. Finally, the energy consumption of the LAO-NCA at 100°C is expected to reduce by 86% compared with a typical STNO-based NCS at the room temperature. |
| first_indexed | 2024-12-13T03:56:40Z |
| format | Article |
| id | doaj.art-c4ff38ef0aff417996c078ecd6bab718 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-13T03:56:40Z |
| publishDate | 2020-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-c4ff38ef0aff417996c078ecd6bab7182022-12-22T00:00:36ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-01-011310.3389/fnins.2019.01429484489LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing SystemHooman Farkhani0Tim Böhnert1Mohammad Tarequzzaman2José Diogo Costa3Alex Jenkins4Ricardo Ferreira5Jens Kargaard Madsen6Farshad Moradi7Integrated Circuits and Electronics Laboratory, Department of Engineering, Aarhus University, Aarhus, DenmarkInternational Iberian Nanotechnology Laboratory, Braga, PortugalInternational Iberian Nanotechnology Laboratory, Braga, PortugalInternational Iberian Nanotechnology Laboratory, Braga, PortugalInternational Iberian Nanotechnology Laboratory, Braga, PortugalInternational Iberian Nanotechnology Laboratory, Braga, PortugalIntegrated Circuits and Electronics Laboratory, Department of Engineering, Aarhus University, Aarhus, DenmarkIntegrated Circuits and Electronics Laboratory, Department of Engineering, Aarhus University, Aarhus, DenmarkDealing with big data, especially the videos and images, is the biggest challenge of existing Von-Neumann machines while the human brain, benefiting from its massive parallel structure, is capable of processing the images and videos in a fraction of second. The most promising solution, which has been recently researched widely, is brain-inspired computers, so-called neuromorphic computing systems (NCS). The NCS overcomes the limitation of the word-at-a-time thinking of conventional computers benefiting from massive parallelism for data processing, similar to the brain. Recently, spintronic-based NCSs have shown the potential of implementation of low-power high-density NCSs, where neurons are implemented using magnetic tunnel junctions (MTJs) or spin torque nano-oscillators (STNOs) and memristors are used to mimic synaptic functionality. Although using STNOs as neuron requires lower energy in comparison to the MTJs, still there is a huge gap between the power consumption of spintronic-based NCSs and the brain due to high bias current needed for starting the oscillation with a detectable output power. In this manuscript, we propose a spintronic-based NCS (196 × 10) proof-of-concept where the power consumption of the NCS is reduced by assisting the STNO oscillation through a microwatt nanosecond laser pulse. The experimental results show the power consumption of the STNOs in the designed NCS is reduced by 55.3% by heating up the STNOs to 100°C. Moreover, the average power consumption of spintronic layer (STNOs and memristor array) is decreased by 54.9% at 100°C compared with room temperature. The total power consumption of the proposed laser assisted STNO-based NCS (LAO-NCS) at 100°C is improved by 40% in comparison to a typical STNO-based NCS at room temperature. Finally, the energy consumption of the LAO-NCA at 100°C is expected to reduce by 86% compared with a typical STNO-based NCS at the room temperature.https://www.frontiersin.org/article/10.3389/fnins.2019.01429/fullneuromorphic computing systemlaserpower efficientCOMSOL multiphysicsspin torque nano-oscillators |
| spellingShingle | Hooman Farkhani Tim Böhnert Mohammad Tarequzzaman José Diogo Costa Alex Jenkins Ricardo Ferreira Jens Kargaard Madsen Farshad Moradi LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System Frontiers in Neuroscience neuromorphic computing system laser power efficient COMSOL multiphysics spin torque nano-oscillators |
| title | LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System |
| title_full | LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System |
| title_fullStr | LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System |
| title_full_unstemmed | LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System |
| title_short | LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System |
| title_sort | lao ncs laser assisted spin torque nano oscillator based neuromorphic computing system |
| topic | neuromorphic computing system laser power efficient COMSOL multiphysics spin torque nano-oscillators |
| url | https://www.frontiersin.org/article/10.3389/fnins.2019.01429/full |
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