Abstract Computational Storage Devices (CSDs), also known as In-Storage Compute or In-
Suit Processing, offer higher computing power than traditional storage devices (SD). The …

Commodity DRAM-based processing-using-memory (PuM) techniques that are supported
by off-the-shelf DRAM chips present an opportunity for alleviating the data movement …

In recent years, there has been a significant advancement in memristor-based neural
networks, positioning them as a pivotal processing-in-memory deployment architecture for a …

Abstract The Discrete Fourier Transform (DFT) is essential for various applications ranging
from signal processing to convolution and polynomial multiplication. The groundbreaking …

In-Memory Computing (IMC) using memristors has gained significant interest in recent years
as it addresses the issue of memory bottleneck in the von Neumann architectures. One of …

Deep Neural Networks (DNNs) have achieved great progress in academia and industry. But
they have become computational and memory intensive with the increase of network depth …

Aligning the entire genome of an organism is a compute-intensive task. Pre-alignment filters
substantially reduce computation complexity by filtering potential alignment locations. The …

Artificial neural networks (ANNs) and spiking neural networks (SNNs) are two general
approaches to achieve artificial intelligence (AI). The former have been widely used in …

Stateful logic is a digital processing-in-memory (PIM) technique that could address von
Neumann memory bottleneck challenges while maintaining backward compatibility with …

In-memory computing using nonvolatile memory is a promising pathway to accelerate data-
intensive applications. While substantial research efforts have been dedicated to executing …