IEEE

MH-SpGEMM: Efficient Sparse General Matrix-Matrix Multiplication on Modern GPUs via Masking and Hashing Cooperative Optimization

Abstract: Sparse General Matrix-Matrix Multiplication (SpGEMM) is a core operation in high-performance computing applications such as algebraic multigrid solvers, machine learning, and graph ...
Nanowerk

Stacked memristor arrays compute Euclidean distance in memory to accelerate self-organizing maps

A novel stacked memristor architecture performs Euclidean distance calculations directly within memory, enabling ...
Le Lézard

MicroCloud Hologram Inc. Utilizes FPGA to Accelerate Tensor Network Computing to Achieve Quantum Spin Models

MicroCloud Hologram Inc. , ("HOLO" or the "Company"), a technology service provider, proposed an innovative hardware ...
IEEE

ScanNow: A Scan Window-Based Sparse Matrix Multiplication Accelerator Design

Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...