As von Neumann computing architectures become increasingly constrained by data-
movement overheads, researchers have started exploring in-memory computing (IMC) …

Artificial intelligence (AI) processes data-centric applications with minimal effort. However, it
poses new challenges to system design in terms of computational speed and energy …

To efficiently implement searching and logic functions with the SRAM-based in-memory
computing (IMC), we need to perform computations on bitlines (BLs)(called compute access) …

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 …

This article obtains fundamental limits on the computational precision of in-memory
computing architectures (IMCs). An IMC noise model and associated signal-to-noise ratio …

With the increasing gap between processing speed and memory bandwidth necessity for
in/near-memory computing has emerged, to ensure high-performance, energy-efficient …

Edge artificial intelligence applications impose rigorous demands on local hardware to
improve throughput and energy efficiency. Computing-in-memory (CIM) architectures …

A compute SRAM requires high speed for both compute access and normal read access.
However, it presents a very distinct bitline discharging behavior in these two modes, thus …

The proposal of compute-in-memory (CIM) is a breakthrough for the traditional von
Neumann architecture to achieve efficient computing research. This architecture has unique …

Frequent to-and-from data transfers in the von Neumann architecture limit the overall
throughput. One of the promising approaches used to overcome von Neumann bottleneck is …