Despite the significant progress made in deep learning on digital computers, their energy
consumption and computational speed still fall short of meeting the standards for brain-like …

Abstract Computation-in-memory (CIM) chips offer an energy-efficient approach to artificial
intelligence computing workloads. Resistive random-access memory (RRAM)-based CIM …

Abstract Compute-in-memory (CIM) accelerators based on emerging memory devices are of
potential use in edge artificial intelligence and machine learning applications due to their …

Compute-in-memory based on resistive random-access memory has emerged as a
promising technology for accelerating neural networks on edge devices. It can reduce …

Lattice-based cryptographic algorithms built on ring learning with error theory are gaining
importance due to their potential for providing post-quantum security. However, these …

Modern data centers suffer from a growing carbon footprint due to insufficient support for
environmental sus-tainability. While hardware accelerators and renewable energy have …