AMD and Intel have now published a full technical specification for ACE — AI Compute Extensions — the most significant overhaul to x86 AI compute in the architecture's history, co-authored by eight ...

AI infrastructure startup Tensordyne has taped out its first commercial accelerator, with fabrication on TSMC's 3nm process ...

Right off the bat, let’s give a shout out to the mathematician propeller-heads who create the transformations that make it possible to do all kinds of high performance computing to simulate, model, ...

Forgive me for starting with a cliché, a piece of finance jargon that has recently slipped into the tech lexicon, but I’m afraid I must talk about “moats.” Popularized decades ago by Warren Buffett to ...

Abstract: General matrix-matrix multiplication (GEMM), serving as a cornerstone of AI computations, has positioned tensor processing engines (TPEs) as increasingly critical components within existing ...

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 ...

Double precision floating point computation (aka FP64) is what keeps modern aircraft in the sky, rockets going up, vaccines effective, and, yes, nuclear weapons operational. But rather than building ...

Optical computing has been limited to vector–matrix multiplications, with matrix–matrix operations requiring wavelength- or time-division multiplexing, reducing energy efficiency and speed. Now, ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations. The purpose of Mesh TensorFlow is to formalize and implement ...