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 …

Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

When applied to artificial intelligence edge devices, the conventionally von Neumann
computing architecture imposes numerous challenges (eg, improving the energy efficiency) …

Non-volatile computing-in-memory macros that are based on two-dimensional arrays of
memristors are of use in the development of artificial intelligence edge devices. Scaling such …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

Satisfying the rapid evolution of artificial intelligence (AI) algorithms requires exponential
growth in computing resources, which, in turn, presents huge challenges for deploying AI …

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 …

We present a 256 256 in-memory compute (IMC) core designed and fabricated in 14-nm
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …

Battery-powered tiny-AI edge devices require large-capacity nonvolatile compute-in-memory
(nvCIM), with multibit input (IN), weight (W), and output (OUT) precision to support complex …

The superior density of passive analog-grade memristive crossbar circuits enables storing
large neural network models directly on specialized neuromorphic chips to avoid costly off …