Artificial intelligence (AI) is accelerating the development of unconventional computing
paradigms inspired by the abilities and energy efficiency of the brain. The human brain …

Approaching limitations of digital computing technologies have spurred research in
neuromorphic and other unconventional approaches to computing. Here we argue that if we …

Human‐machine interfaces (HMIs) play important role in the communication between
humans and robots. Touchless HMIs with high hand dexterity and hygiene hold great …

Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …

To realize molecular-scale electrical operations beyond the von Neumann bottleneck, new
types of multifunctional switches are needed that mimic self-learning or neuromorphic …

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

Zinc oxide (ZnO) nanorods have been extensively investigated, owing to their extraordinary
applications in numerous fields, spatially microchip technology, solar cells, sensors …

Recent successes in deep learning for vision and natural language processing are
attributed to larger models but come with energy consumption and scalability issues. Current …

Neuromorphic devices provide a hardware platform to implement synaptic functions into
artificial electronic devices, which opens a new way to overcome the von Neumann …

Pattern recognition is the core component of the electronic nose (E-nose). Traditional
machine learning algorithms highly rely on the feature data selected manually for model …