With the rapid advancement of artificial intelligence (AI), computing-in-memory (CIM)
structure is proposed to improve energy efficiency (EF). However, previous CIMs often rely …

In this work, we present a novel 8T static random access memory (SRAM)-based compute-in-
memory (CIM) macro for processing neural networks with high energy efficiency. The …

Advances in static random access memory (SRAM)-CIM devices are meant to increase
capacity while improving energy efficiency (EF) and reducing computing latency (). This …

The energy efficiency issue caused by the memory wall in traditional von Neumann
architecture is difficult to reconcile. In-memory computing (CIM) based on emerging …

Advanced artificial intelligence edge devices are expected to support floating-point (FP)
multiply and accumulation operations while ensuring high energy efficiency and high …

This article presents a novel static random access memory computing-in-memory (SRAM-
CIM) structure designed for high-precision multiply-and-accumulate (MAC) operations with …

Computation in memory (CIM) overcomes the von Neumann bottleneck by minimizing the
communication overhead between memory and process elements. However, using …

In this article, we present a spiking neural network (SNN) based on both SRAM processing-
in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent …

Computing in-memory (CIM) is a promising new computing method to solve problems
caused by von Neumann bottlenecks. It mitigates the need for transmitting large amounts of …

Computing-in-memory (CIM) is an attractive approach for energy-efficient deep neural
network (DNN) processing, especially for low-power edge devices. However, today's typical …