Primary Algorithm : Algorithmically, Sparse-Sparse multiplication problems manifests itself in three possible forms:(a) Multiplication of a sparse matrix with a sparse diagonal, sparse block-diagonal, ...

Matrix multiplication is a fundamental operation used in various fields such as computer science, data analysis, graphics, and machine learning. As the size of matrices increases, the computational ...

We’re just a few years into the AI revolution, but AI systems are already improving decades-old computer science algorithms. Google’s AlphaEvolve AI, its latest coding agent for algorithm discovery, ...

Distributed computing has markedly advanced the efficiency and reliability of complex numerical tasks, particularly matrix multiplication, which is central to numerous computational applications from ...

Abstract: Strassen's block-recursive matrix multiplication is amenable to parallelization via distributed recursion. Recently, distributed implementations of Strassen's algorithm using Big-data ...

Multiplying the content of two x-y matrices together for screen rendering and AI processing. Matrix multiplication provides a series of fast multiply and add operations in parallel, and it is built ...

AI training time is at a point in an exponential where more throughput isn't going to advance functionality much at all. The underlying problem, problem solving by training, is computationally ...

Abstract: On-chip optical neural networks (ONNs) have recently emerged as an attractive hardware accelerator for deep learning applications, characterized by high computing density, low latency, and ...