has developed a way to run AI language models without using matrix multiplication. The team has published a paper on the ...

In a new preprint paper, researchers from UC Santa Cruz show that it is possible to eliminate the most computationally expensive element of running large language models, called matrix multiplication, ...

Matrix multiplication, which involves multiplying two rectangular arrays of numbers, is often found at the heart of speech recognition, image recognition, smartphone image processing, compression ...

These models, called "MatMul-free Language Models", aim to achieve this by largely dispensing with resource-intensive matrix multiplications ... Programmable Gate Array (FPGA), which could ...

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 ...