High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...

Matrix multiplication is expensive O(n^3) operations! But what if we could verify the result without doing the full computation? I implemented Freivalds' algorithm in C to probabilistically verify ...

CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 ...

Abstract: Resistive RAM (RRAM) technology has emerged as a viable candidate for artificial intelligence and machine learning applications due to its matrix ...

Imagine driving down a busy highway. You need to check your speed and navigation, but glancing down at the dashboard takes ...

Abstract: Evolutionary multi-task optimization is an emerging research topic in the field of evolutionary computation. It aims to achieve simultaneous optimization of different tasks by dynamically ...

A new holographic computation method could significantly advance augmented‑reality head‑up displays (AR‑HUDs) for vehicles.

New computational holography algorithms cut processing time by over half and enable multi-depth augmented reality displays, a ...