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

This comprehensive review summarizes state of the art, challenges, and prospects of the
neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Processing-in-memory (PIM) is a promising solution to address the" memory wall"
challenges for future computer systems. Prior proposed PIM architectures put additional …

Neuro-inspired architectures based on synaptic memory arrays have been proposed for on-
chip acceleration of weighted sum and weight update in machine/deep learning algorithms …

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 …

Recently, due to the availability of big data and the rapid growth of computing power,
artificial intelligence (AI) has regained tremendous attention and investment. Machine …

Vector-matrix multiplication dominates the computation time and energy for many workloads,
particularly neural network algorithms and linear transforms (eg, the Discrete Fourier …

Graph processing recently received intensive interests in light of a wide range of needs to
understand relationships. It is well-known for the poor locality and high memory bandwidth …

By mimicking the highly parallel biological systems, neuromorphic hardware provides the
capability of information processing within a compact and energy-efficient platform …