Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …

Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …

Analogue in-memory computing (AIMC) with resistive memory devices could reduce the
latency and energy consumption of deep neural network inference tasks by directly …

Compute-in-memory (CiM) is one promising solution to address the memory bottleneck
existing in traditional computing architectures. However, the tradeoff between energy …

Compute-in-memory (nvCIM) macros based on non-volatile memory make it possible for
artificial intelligence (AI) edge devices to perform energy-efficient multiply-and-accumulate …

Advancements in AI led to the emergence of in-memory-computing architectures as a
promising solution for the associated computing and memory challenges. This study …

The unprecedented progress in artificial intelligence (AI), particularly in deep learning
algorithms with ubiquitous internet connected smart devices, has created a high demand for …

For the non-stop demands for a better and smarter society, the number of electronic devices
keeps increasing exponentially; and the computation power, communication data rate, smart …

In-memory computing (IMC) architectures have emerged as a compelling platform to
implement energy-efficient machine learning (ML) systems. However, today, the energy …

Non-volatile computing-in-memory (nvCIM) can potentially meet the ever-increasing
demands on improving the energy efficiency (EF) for intelligent edge devices. However, it …