The rapid increase in information in the big-data era calls for changes to information-
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …

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 …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Data-intensive computing operations, such as training neural networks, are essential for
applications in artificial intelligence but are energy intensive. One solution is to develop …

Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical
way, by enabling machine learning in the industry, business, health, transportation, and …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

Devices with tunable resistance are highly sought after for neuromorphic computing.
Conventional resistive memories, however, suffer from nonlinear and asymmetric resistance …

The explosive growth of machine learning is largely due to the recent advancements in
hardware and architecture. The engineering of network structures, taking advantage of the …

Organic-inorganic halide perovskite quantum dots (PQDs) constitute an attractive class of
materials for many optoelectronic applications. However, their charge transport properties …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …