Modern computers are based on the von Neumann architecture in which computation and
storage are physically separated: data are fetched from the memory unit, shuttled to 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 …

Memristor crossbars offer reconfigurable non-volatile resistance states and could remove
the speed and energy efficiency bottleneck in vector-matrix multiplication, a core computing …

This article provides a review of current development and challenges in brain-inspired
computing with memristors. We review the mechanisms of various memristive devices that …

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 …

With the rise in artificial intelligence (AI), computing systems are facing new challenges
related to the large amount of data and the increasing burden of communication between …

In our brain, information is exchanged among neurons in the form of spikes where both the
space (which neuron fires) and time (when the neuron fires) contain relevant information …

Memristors are promising components for applications in nonvolatile memory, logic circuits,
and neuromorphic computing. In this paper, a novel circuit for memristor-based multilayer …

Neural networks are an increasingly attractive algorithm for natural language processing
and pattern recognition. Deep networks with> 50 M parameters are made possible by …

The on-chip implementation of learning algorithms would speed up the training of neural
networks in crossbar arrays. The circuit level design and implementation of a back …