The inspiration for this column comes not from the epic 1999 film The Matrix, as the title may suggest, but from an episode of Sean Carroll’s Mindscape podcast that I listened to over the summer. The ...

Abstract: This work explores the potential of Quantum Matrix Multiplication (QMM) to accelerate several computational tasks, demonstrating substantial speedups. We present three distinct applications ...

Abstract: Matrix multiplication is a crucial operation in many data-intensive workloads. Given the large size of matrices in today's workloads, it is common to split the computation into tasks ...

QiMeng-GEMM is an innovative approach to automatically generate high-performance matrix multiplication (GEMM) code using LLMs. This codebase provides a comprehensive solution for efficiently computing ...

A growing number of AI processors are being designed around specific workloads rather than standardized benchmarks, ...

The idea isn't novel, but presents major challenges. Tensordyne thinks it has solved them, and promises massive speed and ...

Low Computational Efficiency: The standard implementation breaks down the attention computation into multiple independent steps (such as matrix multiplication and softmax), each requiring frequent ...

On a B200, the nvjet_tst_16x64_64x16_4x1_v_bz_TNN kernel is used, and it takes roughly 8.1 microseconds. On a H200, the nvjet_tst_64x8_64x16_4x1_v_bz_TNT kernel is ...

Cornami delivers breakthrough performance for scalable computing, enabling advanced encryption technologies like FHE to ...

It is well known that large language models (LLMs) often exhibit inconsistencies in their inference results, leading to confusion for users when they ask multiple questions. The research from ...

About 14,000 Himachal government employees serving in 89 categories will be affected by the HP Civil Services (Revised Pay) ...