Artificial intelligence grows more demanding every year. Modern models learn and operate by pushing huge volumes of data ...

The future of computing has arrived in a flash, literally. In A Nutshell Researchers created a computer that performs complex ...

This guide shows how TPUs crush performance bottlenecks, reduce training time, and offer immense scalability via Google Cloud ...

Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...

Aalto University has demonstrated Tensor calculations using light. “Tensor operations are the kind of arithmetic that form ...

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

Recently, a research team led by Prof. Sun Zhong at Peking University reported an analog hardware solution for real-time compressed sensing recovery, which has been published as an article titled ...

AI training time is at a point in an exponential where more throughput isn't going to advance functionality much at all. The underlying problem, problem solving by training, is computationally ...