collaborators

6 papers

cs.AR2026

High-Performance NTT Accelerators for PQC leveraging Unified Redundant Arithmetic and Fine-Tuned Microarchitecture

George Alexakis, Dimitrios Schoinianakis, Giorgos Dimitrakopoulos

Post-quantum cryptography and privacy-preserving technologies are expected to play a central role in future secure communication systems. Lattice-based PQC schemes such as ML-KEM (…

cs.CR2026

Low-Cost Multi-Precision Systolic Arrays for Accelerating FHE NTTs on AI ASICs

George Alexakis, Dimitrios Schoinianakis, Giorgos Dimitrakopoulos

Fully Homomorphic Encryption (FHE) ensures robust data privacy but suffers from prohibitive computational overhead. Accelerating FHE on AI hardware like Tensor Processing Units (TP…

cs.CR2026

MPX: A Unified Systolic Array for Matrix and Polynomial Multiplication

George Alexakis, Dimitrios Schoinianakis, Giorgos Dimitrakopoulos

Polynomial multiplication is a fundamental kernel in Fully Homomorphic Encryption (FHE) and post-quantum cryptography (PQC) and is commonly accelerated through Number Theoretic Tra…

cs.AR2025

High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic

George Alexakis, Dimitrios Schoinianakis, Giorgos Dimitrakopoulos

The Number Theoretic Transform (NTT) is a fundamental operation in privacy-preserving technologies, particularly within fully homomorphic encryption (FHE). The efficiency of NTT co…

cs.AR2025

Efficient Implementation of RISC-V Vector Permutation Instructions

Vasileios Titopoulos, George Alexakis, Chrysostomos Nicopoulos +1

RISC-V CPUs leverage the RVV (RISC-V Vector) extension to accelerate data-parallel workloads. In addition to arithmetic operations, RVV includes powerful permutation instructions t…

cs.AR2025

Register Dispersion: Reducing the Footprint of the Vector Register File in Vector Engines of Low-Cost RISC-V CPUs

Vasileios Titopoulos, George Alexakis, Kosmas Alexandridis +2

The deployment of Machine Learning (ML) applications at the edge on resource-constrained devices has accentuated the need for efficient ML processing on low-cost processors. While…