The definition of a memristor was introduced by Professor L. O. Chua in 1971. By generalizing the definition of a memristor, memristive devices and systems were also proposed. Since the real memristor was implemented at Hewlett-Packard Labs, there has been an increasing interest in memristor and its applications. Memristor displays the relationship between charge and magnetic flux. Nowadays, memristor is therefore considered to be the fourth basic circuit element besides three other classical elements (resistor, inductor, and capacitor). Memristive systems have found potential applications in various areas ranging from physics, biological models to engineering. For example, memristor is potentially used for non-volatile solid-state memory.
Neuromorphic system has been the subject of many classic studies. Because of the rapid development in modern microelectronics, there are opportunities for the electronic implementation of neuromorphic systems. Neuromorphic computing is an important research area that has significant influence on the development of artificial intelligence. There are many applications of neuromorphic computing in computer vision, pattern recognition, natural language processing, and robotics.
Memristor-based neuromorphic computing has been introduced as a new breakthrough technique and is expected to solve current computational bottlenecks. Memristive neuromorphic system exhibits advanced features, such as a much lower power supply voltage requirement, lower power consumption, and a nano-scale size. Therefore, a very promising memristive era is opening for future practical systems.
Besides the rapid development of the field, there are still different issues that should be further investigated. Exploiting the favorable performance merits of mem-elements concerning their non-volatility and switching speed is still an open topic. In addition, the optimization of area and energy dissipation for large array integration architecture is a challenging task.
There is a need to propose memristive deep neural networks combining recent advances in the machine learning areas, such as deep learning, with nonlinear dynamics and novel features of memristor elements. Moreover, there is an increasing demand for a practical solution to integrate such systems in low-cost, energy-saving devices for various Internet of Things (IoT) applications.
This Research Topic aims at representing and discussing advanced topics of neuromorphic mem-computation. We welcome submissions related to such current field focusing on, but not limited to, the following topics:
- Nonlinear analysis of neuromorphic mem-systems.
- Analog and digital memristor-based circuits, systems and architectures.
- Neuromorphic circuits and systems.
- In-memory computing.
- Novel architectures with CMOS integration.
- Memristor-based sensory platforms.
- Nonlinear dynamics, chaos, and complexity.
- Emerging artificial intelligence applications exploiting memristor.
The definition of a memristor was introduced by Professor L. O. Chua in 1971. By generalizing the definition of a memristor, memristive devices and systems were also proposed. Since the real memristor was implemented at Hewlett-Packard Labs, there has been an increasing interest in memristor and its applications. Memristor displays the relationship between charge and magnetic flux. Nowadays, memristor is therefore considered to be the fourth basic circuit element besides three other classical elements (resistor, inductor, and capacitor). Memristive systems have found potential applications in various areas ranging from physics, biological models to engineering. For example, memristor is potentially used for non-volatile solid-state memory.
Neuromorphic system has been the subject of many classic studies. Because of the rapid development in modern microelectronics, there are opportunities for the electronic implementation of neuromorphic systems. Neuromorphic computing is an important research area that has significant influence on the development of artificial intelligence. There are many applications of neuromorphic computing in computer vision, pattern recognition, natural language processing, and robotics.
Memristor-based neuromorphic computing has been introduced as a new breakthrough technique and is expected to solve current computational bottlenecks. Memristive neuromorphic system exhibits advanced features, such as a much lower power supply voltage requirement, lower power consumption, and a nano-scale size. Therefore, a very promising memristive era is opening for future practical systems.
Besides the rapid development of the field, there are still different issues that should be further investigated. Exploiting the favorable performance merits of mem-elements concerning their non-volatility and switching speed is still an open topic. In addition, the optimization of area and energy dissipation for large array integration architecture is a challenging task.
There is a need to propose memristive deep neural networks combining recent advances in the machine learning areas, such as deep learning, with nonlinear dynamics and novel features of memristor elements. Moreover, there is an increasing demand for a practical solution to integrate such systems in low-cost, energy-saving devices for various Internet of Things (IoT) applications.
This Research Topic aims at representing and discussing advanced topics of neuromorphic mem-computation. We welcome submissions related to such current field focusing on, but not limited to, the following topics:
- Nonlinear analysis of neuromorphic mem-systems.
- Analog and digital memristor-based circuits, systems and architectures.
- Neuromorphic circuits and systems.
- In-memory computing.
- Novel architectures with CMOS integration.
- Memristor-based sensory platforms.
- Nonlinear dynamics, chaos, and complexity.
- Emerging artificial intelligence applications exploiting memristor.
