General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …

This article proposes a novel hardware accelerator for the inference task with sparse
convolutional neural networks (CNNs) by building a hardware unit to perform Image to …

Efficient utilization of restrained memory resources is of paramount importance in CPU-
FPGA heterogeneous multiprocessor system-on-chip (HMPSoC)-based system design for …

In 1812, a French mathematician named Jacques Philippe Marie Binet pointed out several
important computations involved the multiplication of two matrices [53]. On November 30 of …

Sparse matrix multiplication (SpMM) plays a critical role in high-performance computing
applications, such as deep learning, image processing, and physical simulation. Field …

This paper proposes a new hardware accelerator for sparse convolutional neural networks
(CNNs) by building a hardware unit to perform the Image to Column (IM2COL) …

This thesis presents novel software/hardware co-design methodologies aimed at
accelerating sparse linear algebra applications within the realm of High-Performance …

This paper presents a data management model targeting heterogeneous distributed
systems integrating reconfigurable accelerators. The purpose of this model is to reduce the …

Linear algebra kernels are widely used in various fields such as machine learning, data
science, physical science, and graph analysis. Many of these applications work with sparse …

In the last few years, the heterogeneous architectures have become dominant in each part of
the computing industry: from heterogeneous GPU accelerators joining multi-core CPUs …