Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

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

Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …

Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge
devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

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 …

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

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 …

Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …

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