CDimension has developed a process for growing molybdenum disulfide (MoS2), a 2D semiconductor, on silicon at a low-enough temperature that it will not damage underlying silicon circuits. That could ...

In a bold challenge to silicon s long-held dominance in electronics, Penn State researchers have built the world s first working CMOS computer entirely from atom-thin 2D materials. Using ...

Metal organic framework (MOF)-based mixed-matrix membranes (MMMs), which embed MOF particles in polymer matrices, combine the advantages of polymeric and inorganic membranes. Multiple previous studies ...

Due to the presence of slow or failed worker computers (called stragglers), distributed matrix multiplication over large clusters may encounter delays. To tackle this issue, Factored Luby Transform ...

algorithms New Breakthrough Brings Matrix Multiplication Closer to Ideal By eliminating a hidden inefficiency, computer scientists have come up with a new way to multiply large matrices that’s faster ...

First of all, thank you very much for the PyTorch Geometric build, I use it all the time and it's very smooth! When debugging the base code, I noticed that for sparse matrix multiplication, you call ...

This repository contains an optimized CUDA-based Matrix Multiplication code written in C++. The code leverages the power of GPU parallel computing to speed up matrix multiplication tasks.

A new research paper titled “Discovering faster matrix multiplication algorithms with reinforcement learning” was published by researchers at DeepMind. “Here we report a deep reinforcement learning ...

DeepMind’s paper also pointed out that AlphaTensor discovers a richer space of matrix multiplication algorithms than previously thought — up to thousands for each size.