Artificial intelligence grows more demanding every year. Modern models learn and operate by pushing huge volumes of data through repeated matrix operations that sit at the heart of every neural ...

Researchers in China published a paper describing a theoretical model for photonic computing that used light particles instead of electrons for faster processing. The team developed “parallel optical ...

There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or ...

What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...

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.

Multiplying the content of two x-y matrices together for screen rendering and AI processing. Matrix multiplication provides a series of fast multiply and add operations in parallel, and it is built ...

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

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Andres Almiray, a serial open-source ...