Frontier development of spintronics-based synapse and neuron for artificial neural networks

这项研究的目的是为人工神经网络开发基于自旋电子学的设备。在过去的一年里，我发表了使用随机磁隧道结(MTJ)来解决整数因式分解的8个概率位(p位)的概率计算的实验结果。最近一年，我探索了p计算的广阔应用空间，以确定其潜力。首先，我们与普渡大学合作，展示了基于MTJ的p-bit可以表示Boltzmann机器并执行机器学习的实验结果。这些结果对人工智能机器学习领域有很大的影响，特别是在需要区域高效机器学习芯片的领域，如边缘计算。来自加利福尼亚州。圣巴巴拉(美国)，我们从理论上证明了MTJ结构可以改变以产生纳秒波动和独立性。这些结果将为构建高集成度、大规模的基于MTJ的p比特网络奠定基础。最后，更快的MTJ波动速度导致更快的求解时间。与实验室中的其他成员一起，我们实验演示了一种具有平面内易轴的MTJ，其波动速度高达8 ns，是世界上波动最快的MTJ。

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.