InfoQ

Maximizing Deep Learning Performance on CPUs using Modern Architectures

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...
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 ...
PC Gamer

AMD, Intel, Microsoft, and Nvidia are all excited about cooperative vectors and what they mean for the future of 3D graphics, but it's going to be a good while before we really ...

At this year's Game Developer Conference in San Francisco, Microsoft the three biggest GPU manufacturers kicked off a week of lectures on advanced graphics techniques with an introduction to the next ...
IEEE

Conflict-Free Symmetric Sparse Matrix-Vector Multiplication on Multicore Architectures

Abstract: Exploiting the numeric symmetry in sparse matrices to reduce their memory footprint is very tempting for optimizing the memory-bound Sparse Matrix-Vector Multiplication (SpMV) kernel.
TechSpot

Microsoft DirectX embraces neural rendering for next-gen graphics

Forward-looking: Microsoft first introduced DirectX alongside Windows 95 as an incentive to drive game developers to support the new GUI-based operating system. The company has never stopped improving ...
IEEE

A Conflict-aware Divide-and-Conquer Algorithm for Symmetric Sparse Matrix-Vector Multiplication

Abstract: Exploiting matrix symmetry to halve memory footprint offers an opportunity for accelerating memory-bound computations like Sparse Matrix-Vector Multiplication (SpMV). However, symmetric SpMV ...
GitHub

Halevi-Shoup Matrix Multiplication

Since homomorphic encryption enables SIMD operations by packing multiple values into a vector of operations and enabling pairwise addition or multiplication operations, one (old) conventional method ...
Semiconductor Engineering

Lower Energy, High Performance LLM on FPGA Without Matrix Multiplication

A new technical paper titled “Scalable MatMul-free Language Modeling” was published by UC Santa Cruz, Soochow University, UC Davis, and LuxiTech. “Matrix multiplication (MatMul) typically dominates ...
acm.org

Solving Sparse Linear Systems Faster than Matrix Multiplication

Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...
news.ucsc

Researchers run high-performing large language model on the energy needed to power a lightbulb

Large language models such as ChaptGPT have proven to be able to produce remarkably intelligent results, but the energy and monetary costs associated with running these massive algorithms is sky high.
marktechpost

This AI Research Discusses Achieving Efficient Large Language Models (LLMs) by Eliminating Matrix Multiplication for Scalable Performance

Most neural network topologies heavily rely on matrix multiplication (MatMul), primarily because it is essential to many basic processes. Vector-matrix multiplication (VMM) is commonly used by dense ...