In this paper, a hybrid memory architecture based on a new array of SRAM and resistive
random-access memory (RRAM) cells is proposed to perform in-memory computing by …

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 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 …

Number Theoretic Transform (NTT) is an essential mathematical tool for computing
polynomial multiplication in promising lattice-based cryptography. However, costly division …

Elliptic curve cryptography (ECC) is widely used in security applications such as public key
cryptography (PKC) and zero-knowledge proofs (ZKP). ECC is composed of modular …

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 …

Recently, four NIST-approved Post-Quantum Cryptography (PQC) algorithms are selected to
be standardized. Three of them are lattice-based cryptographic schemes and feature the …

In the realm of neural network computation, optical neural network accelerators (ONNs)
have emerged as a promising solution, leveraging the inherent speed and parallelism of …

Fully homomorphic encryption (FHE) presents a promising opportunity to remove privacy
barriers in various scenarios including cloud computing and secure database search, by …

Computation-in-memory (CIM) using memristors can facilitate data processing within the
memory itself, leading to superior energy efficiency than conventional von-Neumann …