HfO 2-based resistive switching memory (RRAM) combines several outstanding properties,
such as high scalability, fast switching speed, low power, compatibility with complementary …

In-memory computing (IMC) refers to non-von Neumann architectures where data are
processed in situ within the memory by taking advantage of physical laws. Among the …

Resistive switching memory (RRAM) is a promising technology for embedded memory and
its application in computing. In particular, RRAM arrays can provide a convenient primitive …

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 …

In-memory computing is a promising non-von Neumann approach to perform certain
computational tasks efficiently within memory devices by exploiting their physical attributes …

Analog in-memory computing (AIMC) using memristive devices is considered a promising
Non-von Neumann approach for deep learning (DL) inference tasks. However, inaccuracies …

Non-volatile memory (NVM) such as RRAM and PCM has become the key component in
high energy efficiency computing-in-memory (CIM) architectures. However, the computing …

Basecalling, an essential step in many genome analysis studies, relies on large Deep
Neural Network s (DNN s) to achieve high accuracy. Unfortunately, these DNN s are …

Computation-in-memory (CIM) paradigm leverages emerging memory technologies such as
resistive random access memories (RRAMs) to process the data within the memory itself …

Neuromorphic computing has emerged as an alternative computing paradigm to address
the increasing computing needs for data-intensive applications. In this context, resistive …