In the cloud computing era, privacy protection is becoming pervasive in a broad range of
applications (eg, machine learning, data mining, etc). Fully Homomorphic Encryption (FHE) …

Homomorphic Encryption (HE) enables users to securely outsource both the storage and
computation of sensitive data to untrusted servers. Not only does HE offer an attractive …

Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and
engineering, graph processing, and machine learning applications. Much work on SpMV …

Fully homomorphic encryption (FHE) is a cryptographic technology capable of resolving
security and privacy problems in cloud computing by encrypting data in use. However, FHE …

Specialized hardware accelerators continue to be a source of performance improvement.
However, such specialization comes at a programming price. The fundamental issue is that …

Matrix-multiplication units (MXUs) are now prevalent in every computing platform. The key
attribute that makes MXUs so successful is the semiring structure, which allows tiling for both …

This paper evaluates the efficacy of recent commercial processing-in-memory (PIM)
solutions to accelerate fast Fourier transform (FFT), an important primitive across several …

The challenge to fully exploit the potential of existing and upcoming scientific instruments
like large single-dish radio telescopes is to process the collected massive data effectively …

Modern GPUs commonly employ specialized matrix multiplication units (MXUs) to
accelerate matrix multiplication, the core computation of deep learning workloads. However …

This survey summarizes the software implementation knowledge of the Number Theoretic
Transform (NTT)—a major subroutine of lattice-based cryptosystems. The NTT is a special …