Neural network-based branch prediction techniques represent a significant advancement in processor architecture, where machine learning models replace traditional, heuristic-based mechanisms to ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...

Neural network approximation techniques have emerged as a formidable approach in computational mathematics and machine learning, providing robust tools for approximating complex functions. By ...

As proposed and demonstrated by the Los Alamos team, the architectures and techniques proposed to mitigate or altogether ...

On Tuesday, the Royal Swedish Academy of Sciences awarded the 2024 Nobel Prize in Physics to John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto for their ...

The advent of high-density recording technologies, such as Neuropixels and large-scale calcium imaging, has provided an unprecedented look into the ...

Nvidia researchers have proposed a neural compression method for material textures that, according to results reported in ...

A new technical paper titled “A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware” was published by researchers at Politecnico di Milano. “Executing Spiking Neural Networks (SNNs) on ...

Nvidia researchers have proposed a neural network-based method for compressing material textures that, in results reported in ...

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

In its "Tuscan Wheels" demo, the company showed VRAM usage dropping from roughly 6.5GB with traditional BCN-compressed ...