| 研究実績の概要 |
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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