Prasun Chaudhuri speaks to expert Subrata Das about the opportunities. Das is currently an adjunct faculty member at ...

Vector databases are at the heart of this, because that’s how data created by AI modelling is stored and from where it is accessed during AI inference.. In this article, we look at vector ...

In getting rid of matrix multiplication and running their algorithm on custom hardware, the researchers found that they could power a billion-parameter-scale language model on just 13 watts, about ...

Matrix multiplication (MatMul) is a fundamental operation in most neural networks, primarily because GPUs are highly optimized for these computations. Despite its critical role in deep learning, ...

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The aim of this study was to integrate the simplicity of structured sparsity into existing vector execution flow and vector processing units (VPUs), thus expediting the corresponding matrix ...

Photonic innovation: researchers in the US have created an optical metamaterial that can perform vector–matrix multiplication. (Courtesy: iStock/Henrik5000) A new silicon photonics platform that can ...

"Matrix multiplication advancement could lead to faster, more efficient AI models" ... Often these blocks become 2-D rather than being a simple 1-D vector of numbers.

A Laser Focus. In 1986, Strassen had another big breakthrough when he introduced what’s called the laser method for matrix multiplication. Strassen used it to establish an upper value for omega of ...

Using NumPy for array and matrix math in Python. Many mathematical operations, especially in machine learning or data science, involve working with matrixes, or lists of numbers.