Efforts to design devices emulating complex cognitive abilities and response processes of
biological systems have long been a coveted goal. Recent advancements in flexible …

Research on electronic devices and materials is currently driven by both the slowing down
of transistor scaling and the exponential growth of computing needs, which make present …

Software implementations of brain-inspired computing underlie many important
computational tasks, from image processing to speech recognition, artificial intelligence and …

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

Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

General‐purpose computers usually use logic gate computing units based on
complementary metal oxide semiconductors (CMOS). Due to the separate memory and …

Experimental demonstration of resistive neural networks has been the recent focus of
hardware implementation of neuromorphic computing. Capacitive neural networks, which …

The von Neumann computing architecture consists of separated processing and memory
elements; it is too bulky and energy-intensive to be implemented in the upcoming artificial …

Compared with the traditional von Neumann architecture, neural systems have many
distinctive properties including parallelism, low-power consumption, fault tolerance, self …

We survey recent progress in the use of analog memory devices to build neuromorphic
hardware accelerators for deep learning applications. After an overview of deep learning …