Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …

Analogue in-memory computing (AIMC) with resistive memory devices could reduce the
latency and energy consumption of deep neural network inference tasks by directly …

Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Ferroelectric materials have shown great value in the modern semiconductor industry and
are considered important function materials due to their high dielectric constant and tunable …

Non‐von‐Neumann computing using neuromorphic systems based on two‐terminal
resistive nonvolatile memory elements has emerged as a promising approach, but its full …

The escalating demand for artificial intelligence (AI), the internet of things (IoTs), and energy-
efficient high-volume data processing has brought the need for innovative solutions to the …

Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

Deep neural networks (DNNs) consist of layers of neurons interconnected by synaptic
weights. A high bit-precision in weights is generally required to guarantee high accuracy in …