— We consider memory subsystem optimizations for improving the performance of sparse scientific computation while reducing the power consumed by the CPU and memory. We first co...
In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extract...
We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more th...
In this paper, we propose a reconfigurable hardware accelerator for fixed-point-matrix-vector-multiply/add operations, capable to work on dense and sparse matrices formats. The pr...
We present an architecture and an implementation of an FPGA-based sparse matrix-vector multiplier (SMVM) for use in the iterative solution of large, sparse systems of equations ar...
Yousef El-Kurdi, Warren J. Gross, Dennis Giannacop...