Sparse Matrix-Vector Multiplication (SpMxV) is a widely used mathematical operation in
many high-performance scientific and engineering applications. In recent years, tuned …

We present the design and implementation of a universal, single-bit stream library for
accelerating matrix-vector multiplication using FPGAs. Our library handles multiple matrix …

With the end of Dennard scaling and Moore's law, it is becoming increasingly difficult to build
hardware for emerging applications that meet power and performance targets, while …

Stochastic computing (SC) is a re-emerging technique to process probability data encoded
in digital bit-streams. Its main advantage is that arithmetic operations can be implemented by …

Sparse matrix vector multiplication (SpMV) is an important kernel in many areas of scientific
computing, especially as a building block for iterative linear system solvers. We study how …

Graphics processing units (GPUs) have delivered a remarkable performance for a variety of
high performance computing (HPC) applications through massive parallelism. One such …

Iterative solutions of sparse linear systems and sparse eigenvalue problems have a
fundamental role in vital fields of scientific research and engineering. The crucial computing …

This paper presents a new power assist suit to reduce the burden of a caregiver's waist for a
transfer work in nursing care. The proposed assist suit is a lightweight and inexpensive. In …

Computations involving matrices form the kernel of a large spectrum of computationally
demanding applications for which FPGAs have been utilized as accelerators. Their …

In diverse domains, the scientific applications requires severe computing algebra routines.
The matrix multiplication presents an indispensable mathematical operation in many high …