Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …

We dispel with “street wisdom” regarding the practical implementation of Strassen's
algorithm for matrix-matrix multiplication (DGEMM). Conventional wisdom: it is only practical …

Strassen's algorithm (1969) was the first sub-cubic matrix multiplication algorithm. Winograd
(1971) improved the leading coefficient of its complexity from 6 to 7. There have been many …

Conventional Graphics Processing Unit (GPU) implementations of Strassen's algorithm
(Strassen) rely on the existing high-performance matrix multiplication (gemm), trading space …

Optimizing the operation of rail networks using simulations is an on-going task where
heuristic methods such as Genetic Algorithms have been applied. However, these …

Matrix multiplication is one of the most extensively used kernels in scientific computing.
Although subcubic algorithms exist, most high performance implementations are based on …

Popular deep neural networks (DNNs) spend the majority of their execution time computing
convolutions. The Winograd family of algorithms can greatly reduce the number of arithmetic …

Matrix multiplication (GEMM) is a core operation to numerous scientific applications.
Traditional implementations of Strassen-like fast matrix multiplication (FMM) algorithms often …

In modern day VLSI system design memories are the vital blocks and they need to be
thoroughly investigated with respect to area, power and performance before their fabrication …