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

Abstract: Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represents a ...

Use left and right arrow keys to seek audio. Intel and AMD have partnered to standardize a new x86 ISA extension to supercharge AI performance. Under the x86 Ecosystem Advisory Group (EAG), AI Compute ...

Nvidia’s position as the dominant supplier of AI chips may be under threat from a specialised chip pioneered by Google, with reports suggesting companies like Meta and Anthropic are looking to spend ...

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 ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

Google DeepMind today pulled the curtain back on AlphaEvolve, an artificial-intelligence agent that can invent brand-new computer algorithms — then put them straight to work inside the company's vast ...

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 ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Matrix multiplications (MatMul) are the ...