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 …

Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study,
such as visual recognition, natural language processing, autonomous vehicles, and …

Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …

Artificial intelligence edge devices should offer high inference accuracy and rapid response
times, as well as being energy efficient. Ensuring the security of these devices against …

Battery-powered edge-AI devices require nonvolatile computing-in-memory (nvCIM) macros
for nonvolatile data storage and multiply-and-accumulate (MAC) operations. High inference …

Efficient edge computing, with sufficiently large on-chip memory capacity, is essential in the
internet-of-everything era. Nonvolatile computing-in-memory (nvCIM) reduces the data …

Compute-in-Memory (CIM) using emerging nonvolatile (eNVM) memory technologies, such
as resistive random-access memory (RRAM), has been shown by several implemented …

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 …

Memristors show great potential for being integrated into CMOS technology and provide
new approaches for designing computing-in-memory (CIM) systems, brain-inspired …

Nonvolatile memory (NVM)-based computing-in-memory (nvCIM) is a promising candidate
for artificial intelligence (AI) edge devices to overcome the latency and energy consumption …