You'll be more productive if you can see what you need to do, and when you need to do it. Lindsey Ellefson is Lifehacker’s Features Editor. She currently covers study and productivity hacks, as well ...

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 ...

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 ...

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 ...

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 ...

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.

A team of software engineers at the University of California, working with one colleague from Soochow University and another from LuxiTec, has developed a way to run AI language models without using ...

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...