The potential of artificial intelligence and novel computing architecture can be exploited in the immediate future by means of new materials and new devices which fulfill the special requirements e.g. higher scalability, higher bandwidth, lower power consumptions. It is with the eager demand in both the electronics and systems era to subvert the bottleneck in current CPU architecture i.e. von-Neumann architecture. To realize the new computing paradigms which subvert the memory wall in von Neumann bottleneck, the new computing configurations e.g. neuromorphic computing, edge computing, in-memory computing are attracting a considerable amount of attentions. In this era, non-volatile memory technology using emerging new materials and device physics for implementation of neuromorphic systems and data-centric computing are promising ways to achieve the goals and requirements of next generation energy-efficient high performance computing applications. Among them, the resistvie random access materials based on the resistive switching in oxide and dielectric as one of the candidates for the artificial neural systems and biological emulation. To emulate the biological neural system, the artificial neural systems are also attracting great of interests which is on the basis of the principles of learning e.g. Hebbian learning, memory engram and assembly, synaptic competition, synaptic sampling etc. The artificial neuro network architecture on the basis of new device and materials has been progressively proposed to implement the learning principles (e.g. supervised learning, unsupervised learning, reinforcement learning, reservoir learning, one-shot and few-shot learning etc.) To achieve the neuromorphic functionable electronics, the new materials and device design are widely required e.g. low dimensional materials, solid state dielectrics, flexible resistive switching materials etc. The aim of this collection is to provide a platform for interdisciplinary research into brain-inspired computing with emerging physical understanding on the basis of synaptic and neural behaviors in the device and the systems. It will include studies in areas, but not limited to the biological emulation modeling, materials and physics analytics, emerging memory devices with neural responses, neuromorphic circuits, advanced arithmetic operations for logic applications, ternary content-addressable memory, and novel computing paradigms. The emerging electronics developed with new materials, new structures, and new configurations will be next generation candidates for low power brain inspired computing, highly feasible human-machine interfaces, and edge computing applications.
The potential of artificial intelligence and novel computing architecture can be exploited in the immediate future by means of new materials and new devices which fulfill the special requirements e.g. higher scalability, higher bandwidth, lower power consumptions. It is with the eager demand in both the electronics and systems era to subvert the bottleneck in current CPU architecture i.e. von-Neumann architecture. To realize the new computing paradigms which subvert the memory wall in von Neumann bottleneck, the new computing configurations e.g. neuromorphic computing, edge computing, in-memory computing are attracting a considerable amount of attentions. In this era, non-volatile memory technology using emerging new materials and device physics for implementation of neuromorphic systems and data-centric computing are promising ways to achieve the goals and requirements of next generation energy-efficient high performance computing applications. Among them, the resistvie random access materials based on the resistive switching in oxide and dielectric as one of the candidates for the artificial neural systems and biological emulation. To emulate the biological neural system, the artificial neural systems are also attracting great of interests which is on the basis of the principles of learning e.g. Hebbian learning, memory engram and assembly, synaptic competition, synaptic sampling etc. The artificial neuro network architecture on the basis of new device and materials has been progressively proposed to implement the learning principles (e.g. supervised learning, unsupervised learning, reinforcement learning, reservoir learning, one-shot and few-shot learning etc.) To achieve the neuromorphic functionable electronics, the new materials and device design are widely required e.g. low dimensional materials, solid state dielectrics, flexible resistive switching materials etc. The aim of this collection is to provide a platform for interdisciplinary research into brain-inspired computing with emerging physical understanding on the basis of synaptic and neural behaviors in the device and the systems. It will include studies in areas, but not limited to the biological emulation modeling, materials and physics analytics, emerging memory devices with neural responses, neuromorphic circuits, advanced arithmetic operations for logic applications, ternary content-addressable memory, and novel computing paradigms. The emerging electronics developed with new materials, new structures, and new configurations will be next generation candidates for low power brain inspired computing, highly feasible human-machine interfaces, and edge computing applications.