For the first time, a research team has demonstrated an artificial intelligence semiconductor technology that integrates the ...

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 ...

Tensordyne says logarithmic computing could reduce AI inference costs and power demands, offering an alternative to conventional chip designs.

Abstract: Transformers are at the core of modern AI nowadays. They rely heavily on matrix multiplication and require efficient acceleration due to their substantial memory and computational ...

Abstract: The parallelism and analog computing features of neuromorphic systems bring great challenges in developing a compact model of analog resistive random access memory (RRAM). In this article, ...

Artificial intelligence systems, such as large language models (LLMs) and convolutional neural networks (CNNs), can analyze large amounts of data and rapidly generate desired content or identify ...

Imagine you’re sitting in on a fourth-grade math class, witnessing a multiplication lesson. Instead of splitting the room between fast finishers and students who still need support, the teacher gives ...

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 ...

A startup hopes to challenge Nvidia, AMD, and Intel with a chip that wrangles probabilities rather than 1s and 0s. The startup’s chips work in a fundamentally different way than chips from Nvidia, AMD ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. A recent in situ transmission electron microscopy (TEM) study ...