A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing
A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing
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DOI：
10.1002/aisy.202200123
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发表时间：
2022-09-20
影响因子：
7.4
通讯作者：
Nakajima, Kohei
中科院分区：
文献类型：
--
作者：
Akashi, Nozomi;Kuniyoshi, Yasuo;Nakajima, Kohei
人工智能领域的快速发展增加了对神经形态计算硬件及其信息处理能力的需求。自旋电子器件是神经形态计算硬件的一个有前途的候选者，并且由于其高抗辐射性而可以在极端环境中使用。提高神经形态计算的信息处理能力是实现的一个重要挑战。在此，提出了一种新的神经形态计算框架，使用自旋电子器件。这个框架被称为耦合自旋电子学水库（CSR）计算，并利用耦合自旋力矩振荡器作为计算资源的高维动力学。通过数值实验分析了CSR的各种分叉与其信息处理能力之间的关系，发现CSR的某些配置使自旋电子学库的信息处理能力接近甚至超过机器学习网络的标准水平。我们的方法的有效性通过传统的机器学习基准测试和边缘计算在真实的物理实验中使用气动人工肌肉可穿戴设备来证明，这些设备可以在各种环境中辅助人类操作。这项研究显着推进神经形态计算的实际应用的可用性。
The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information-processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information-processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high-dimensional dynamics of coupled spin-torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information-processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information-processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle-based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses.