Memristors, promising nanoelectronic devices with in-memory resistive switching behavior
that is assembled with a physically integrated core processing unit (CPU) and memory unit …

In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory
architecture based on resistive random-access memory (RRAM), named M3D-LIME. The …

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby
enabling brain-inspired neuromorphic computing to overcome the limitations of the von …

Artificial neural networks (ANNs) have gained considerable momentum in the past decade.
Although at first the main task of the ANN paradigm was to tune the connection weights in …

The von Neumann bottleneck has long been a significant obstacle to the advancement of
the era of intelligent computing. Neuromorphic devices are considered a promising solution …

The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with
past experiences, with fused memory and processing. In contrast, AI models are static …

In this paper, we combine a Content-Addressable Memory (CAM) encoding scheme
previously proposed for analog electronic CAMs (E-CAMs) with optical multiplexing …

In-memory computing (IMC) using emerging nonvolatile devices has received considerable
attention due to its great potential for accelerating artificial neural networks and machine …

There is a growing demand for low-power, autonomously learning artificial intelligence (AI)
systems that can be applied at the edge and rapidly adapt to the specific situation at …

Abstract Analog In-memory Computing (IMC) has demonstrated energy-efficient and low
latency implementation of convolution and fully-connected layers in deep neural networks …