DG Matrix raises $60M to make data center power smarter

The startup is making solid-state transformers capable of intelligently aggregating power from a number of different sources ...
GitHub

[AMDGPU][Codegen] Optimizing skinny GEMM workloads with sparse matrix instructions

This issue is an evolving document describing the design and implementation of using sparse matrix instructions for optimizing skinny GEMM on AMDGPU in IREE. Using FP8 as an example: currently skinny ...
Frontiers

Fast simulation of array laterlog utilizing optimized computational model and neural networks

A new method has been developed to rapidly simulate array laterolog (ALL) responses in invaded formations drilled by deviated wells. This method is characterized by two key aspects: simplification of ...
IEEE

Accelerating Sparse Matrix-Matrix Multiplication by Adaptive Batching Strategy on MGPUSim

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is a widely used algorithm in Machine Learning, particularly in the increasingly popular Graph Neural Networks (GNNs). SpMM is an essential ...
Hosted on MSN

Allies and algorithms: The matrix and partnerships shaping AI’s future

CNBC's Squawk Box Asia Martin Soong and Chery Kang talk about AMD's chip supply deal with OpenAI, plus the web of alliances, cross shareholdings and the money loop that could shape the AI space. Major ...
C&EN

QCMaquis 4.0: Multipurpose Electronic, Vibrational, and Vibronic Structure and Dynamics Calculations with the Density Matrix Renormalization Group

You are free to share (copy and redistribute) this article in any medium or format within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must ...
marktechpost

RXTX: A Machine Learning-Guided Algorithm for Efficient Structured Matrix Multiplication

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...