Bioinspired computation systems can achieve artificial intelligence, bypassing fundamental
bottlenecks and cost constraints. Computational frameworks suited for temporal/sequential …

Growth of data eases the way to access the world but requires increasing amounts of energy
to store and process. Neuromorphic electronics has emerged in the last decade, inspired by …

The booming development of artificial intelligence (AI) requires faster physical processing
units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged …

Conventional artificial intelligence (AI) systems are facing bottlenecks due to the
fundamental mismatches between AI models, which rely on parallel, in-memory, and …

Multilevel resistive switching in memristive devices is vital for applications in non-volatile
memory and neuromorphic computing. In this study, we report on the multilevel resistive …

Reservoir computing (RC), as a framework for artificial intelligence (AI) computation, is
derived from recurrent neural networks, but with higher efficiency benefits from its much …

Synaptic devices based on 2D materials are being considered as potential solutions to
mimic the behavior of synapses in neuromorphic computing. However, a scalable and …

Neuromorphic computing devices offer promising solutions for next‐generation computing
hardware, addressing the high throughput data processing demands of artificial intelligence …

The development of neuromorphic devices is a pivotal step in the pursuit of low‐power
artificial intelligence. A synaptic analog is one of the building blocks of this vision. The …

Reservoir computing (RC)-based neuromorphic applications exhibit extremely low power
consumption, thus challenging the use of deep neural networks in terms of both …