Tensordyne Claims 8x AI Efficiency Boost Over NVIDIA Using Logarithmic Math

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

Say hello to muhūrta mathematics

By institutionalising muhūrta s within mathematics, the UGC is effectively telling students that astrological determinism is ...
IEEE

FPGA based Matrix Multiplication Accelerator

Abstract: The demand for high-speed matrix multiplication continues to grow due to recent developments in images processing, graphics processing, digital signal processing and communication via ...
IEEE

WireLightning: Harnessing Capacitances for In-Transit Massively Parallel Matrix Multiplication

Abstract: Analog computing-in-memory accelerators promise ultra-low-power, on-device AI by reducing data transfer and energy usage. Yet inherent device variations and high energy consumption for ...
GitHub

QiMeng-GEMM: Automatically Generating High-Performance Matrix Multiplication Code by Exploiting Large Language Models

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 ...
GitHub

eminsafa/pytorch-cuda-compatibility

1.13.0 3.7+ 11.6, 11.7 0.13.0 0.14.0 1.12.1 3.7+ 10.2, 11.3, 11.6 0.12.1 0.13.1 1.12.0 3.7+ 10.2, 11.3, 11.6 0.12.0 0.13.0 1.11.0 3.6+ 10.2, 11.3 0.11.0 0.12.0 1.10.1 ...