Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …

The superior density of passive analog-grade memristive crossbar circuits enables storing
large neural network models directly on specialized neuromorphic chips to avoid costly off …

To efficiently deploy machine learning applications to the edge, compute-in-memory (CIM)
based hardware accelerator is a promising solution with improved throughput and energy …

Memristors show great potential for being integrated into CMOS technology and provide
new approaches for designing computing-in-memory (CIM) systems, brain-inspired …

Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting
considerable attention from academia and the industry. Although it is not completely …

Resistive random access memory (RRAM)-based compute-in-memory (CIM) has shown
great potential for accelerating deep neural network (DNN) inference. However, device …

Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of
multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware …

For state-of-the-art artificial intelligence (AI) accelerators, there have been large advances in
both all-digital and analog/mixed-signal circuit-based designs. This article presents a …

The high computational complexity and a large number of parameters of deep neural
networks (DNNs) become the most intensive burden of deep learning hardware design …