A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing

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

Stabilization of Vehicle Dynamics by Tire Digital Control—Tire Disturbance Control Algorithm for an Electric Motor Drive System

We propose an algorithm with disturbance control for tires on electric vehicles (EVs) so as to improve the steering stability of the vehicle. The effect was validated on EVs equipped with twin independent electric motors on a skid pad. The algorithm with the disturbance controller can remove the external noise generated on tires in order to suppress the abrupt slip and micro vibration generated between the tire and road surface, especially on low friction surfaces at the critical speed of the vehicle. The effective frequency corresponded to tire scale length. The effect was verified by the fact that the hysteresis loop with control on the chart of steer angle and yaw rate showed a smaller loop than those without control. The hysteresis loop with control also appeared at the oversteering area, which can be interpreted as evidence that the algorithm can make the vehicle more stable and gain faster speed on the skid pad. It is concluded that the tire digital control works well without any information from sensors on the vehicle body and without any cooperative control between tires

Embedding bifurcations into pneumatic artificial muscle

Harnessing complex body dynamics has been a long-standing challenge in robotics. Soft body dynamics is a typical example of high complexity in interacting with the environment. An increasing number of studies have reported that these dynamics can be used as a computational resource. This includes the McKibben pneumatic artificial muscle, which is a typical soft actuator. This study demonstrated that various dynamics, including periodic and chaotic dynamics, could be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics that are not presented in training data could be embedded by using this capability of bifurcation embeddment. This implies that it is possible to embed various qualitatively different patterns into pneumatic artificial muscle by learning specific patterns, without the need to design and learn all patterns required for the purpose. Thus, this study sheds new light on a novel pathway to simplify the robotic devices and training of the control by reducing the external pattern generators and the amount and types of training data for the control

A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing

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