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

Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …

This article proposes a general-purpose hybrid in-/near-memory compute SRAM (CRAM)
that combines an 8T transposable bit cell with vector-based, bit-serial in-memory arithmetic …

In-memory computing seeks to minimize data movement and alleviate the memory wall by
computing in-situ, in the same place that the data is located. One of the key emerging …

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

In-memory computing establishes a new and promising computing paradigm aimed at
solving problems caused by the von Neumann bottleneck. It eliminates the need for frequent …

Area and power-constrained edge devices are increasingly utilized to perform compute
intensive workloads, necessitating increasingly area and power-efficient accelerators. In this …

The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …

This paper presents 6T SRAM cell-based bit-parallel in-memory computing (IMC)
architecture to support various computations with reconfigurable bit-precision. In the …

Brain-inspired computing is a growing and interdisciplinary area of research that
investigates how the computational principles of the biological brain can be translated into …