The Next Platform

Tensordyne Converts AI Matrix Math To Logs To Crank Up Inference Oomph

Transformations are the key to such codes, and they rely on math that predates computing as we know it by centuries. There ...
Nature

In-memory processing takes spectrometers to the edge

A memristor chip can provide fast and energy-efficient spectrum analysis by using robust and denoised in-memory computing memory arrays. You have full access to this article via your institution. Fig.
GitHub

ROCm Examples

Tutorials contains the code accompanying the HIP Tutorials that can be found in the HIP documentation. For a full overview over the examples see the section repository contents. Only a subset of the ...
C&EN

Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units and a Mixed Precision Matrix Multiplication Library

§Contributed equally to this work. Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These ...
LinkedIn

Matrix Chain Multiplication Problem - Dynamic Programming

Day 129 of 160 – Matrix Chain Multiplication 🔗🧮 Hello connections! 👋 Today’s challenge was from the Dynamic Programming vault — the classic Matrix Chain Multiplication problem. 🧠 Problem Overview: ...
C&EN

VeloxChem: GPU-Accelerated Fock Matrix Construction Enabling Complex Polarization Propagator Simulations of Circular Dichroism Spectra of G-Quadruplexes

PDC Center for High Performance Computing, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden Division of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, ...
Yahoo

Scientists use Matrix-style learning to ‘write’ skills into human brain noninvasively

Add Yahoo as a preferred source to see more of our stories on Google. A team of researchers from the University of Rochester, Yale University, and Princeton University has made a big stride in ...
IEEE

A Performance Modeling and Optimization Analysis Tool for Sparse Matrix-Vector Multiplication on GPUs

Abstract: This paper presents a performance modeling and optimization analysis tool to predict and optimize the performance of sparse matrix-vector multiplication (SpMV) on GPUs. We make the following ...
Nature

Linear matrix genetic programming as a tool for data-driven black-box control-oriented modeling in conditions of limited access to training data

Autonomous Mobile Robots (AMR) are the answer to the needs of the armed forces, logistics companies, agricultural industry, healthcare institutions, and warehouse systems in terms of improving ...
Scientific Research Publishing

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique ()

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing.
IEEE

Programming Weights to Analog In-Memory Computing Cores by Direct Minimization of the Matrix-Vector Multiplication Error

Abstract: Accurate programming of non-volatile memory (NVM) devices in analog in-memory computing (AIMC) cores is critical to achieve high matrix-vector multiplication (MVM) accuracy during deep ...