Large language models such as ChaptGPT have proven to be able to produce remarkably intelligent results, but the energy and monetary costs associated with running these massive algorithms is sky high.

What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...

Distributed computing has markedly advanced the efficiency and reliability of complex numerical tasks, particularly matrix multiplication, which is central to numerous computational applications from ...

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 ...

Cutting corners: Researchers from the University of California, Santa Cruz, have devised a way to run a billion-parameter-scale large language model using just 13 watts of power – about as much as a ...

Engineers at MIT have turned one of computing’s biggest headaches, waste heat, into the main act. By sculpting “dust-sized” silicon structures that steer heat as precisely as electrical current, they ...

The deep neural network models that power today’s most demanding machine-learning applications are pushing the limits of traditional electronic computing hardware, according to scientists working on a ...

A new publication from Opto-Electronic Technology; DOI   10.29026/oet.2025.250011, discusses integrated photonic synapses, neurons, memristors, and neural networks for photonic neuromorphic computing.