Abstract: Efficiently synthesizing an entire application that consists of multiple algorithms for hardware implementation is a very difficult and unsolved problem. One of the main challenges is the ...

Agencies are getting more information on how to implement the recently finalized “rule of many.” The federal hiring strategy, several years in the making, aims to create broader pools of qualified job ...

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: 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 ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

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 ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

Your average daily heart rate is a useful metric; so is your daily step count. Combining the two might be even better. By Matt Richtel Many people use a smartwatch to monitor their cardiovascular ...

Join host Rob Lipsett and special guest Jesse Meester on The Game Plan podcast as they reveal the 3 powerful steps to escape the Matrix and create a life of freedom and success. In this episode, Rob ...