Frontier development of spintronics-based synapse and neuron for artificial neural networks
| Project/Area Number |
19J12206
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Review Section |
Basic Section 21060:Electron device and electronic equipment-related
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| Research Institution | Tohoku University |
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Principal Investigator |
BORDERS William 東北大学, 工学研究科, 特別研究員(DC2)
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| Project Period (FY) |
2019-04-25 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 2020: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2019: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Spintronics / Probabilistic Computing / Neural Networks / Magnetic Tunnel Junction / Boltzmann Machine / Integer Factorization |
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| Outline of Research at the Start |
This work proposes an interdisciplinary study into the possibilities of spintronics devices as synapses/neurons for artificial neural networks (ANN). Demonstration of each device's basic operation and establishment of its importance for ANN applications are addressed. After establishing both devices, the synapse/neuron will be implemented into circuits to demonstrate their characteristics, involving connection of identical devices to perform asynchronous operations.
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| Outline of Annual Research Achievements |
The objective of this research was to developed spintronics-based devices for artificial neural networks. In the previous year I published experimental results of a probabilistic computing using stochastic magnetic tunnel junctions (MTJ) to solve integer factorization using 8 probabilistic bits (p-bits).
In this recent year, I have explored p-computing’s vast application space to determine it’s potential. First, in collaboration with Purdue Univ., we showed experimental results that MTJ-based p-bits can represent a Boltzmann machine and perform machine learning. These results have large implications in the realm of AI machine learning, especially in areas where area-efficient machine learning chips are desired, such as edge computing.
Second, in a work with the Univ. of Calif. Santa Barbara (USA), we showed theoretically that the MTJ structure can be changed to produce nanosecond fluctuations and independence. These results are will lead to the foundation of building highly-integrated, large-scale MTJ-based p-bit networks.
Finally, faster MTJ fluctuation speed leads to faster solution times. Along with other members in the lab, we experimentally demonstrated an MTJ with an in-plane easy axis with fluctuation speeds as fast as 8 ns, the world’s fastest fluctuating MTJ.
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| Research Progress Status |
令和2年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和2年度が最終年度であるため、記入しない。
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Report
(2 results)
Research Products
(11 results)
