Vienna startup Ora Computing raised €3.5M and proved a 70-billion-parameter large language model can be compressed for under ...

Medical artificial intelligence (AI) faces a fundamental challenge: uncertainty quantification. Artificial neural networks ...

Version 5.0 Modernizes DNN Engine, Adds LLM/VLM Support, and Enhances Core, Hardware Acceleration, and 3D Stack.

Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.

A research team led by Professor Han Zhang at Shenzhen University has pioneered a novel optical neural network that learns like a living organism—without relying on traditional computing algorithms.

Abstract: Quantization is a neural network compression technique that effectively improves the deployment performance on inference hardware. Fixed-point quantization methods use the same bit-width for ...

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language translation. A quantum counterpart—known as a quantum convolutional neural ...

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...

Huawei’s Computing Systems Lab in Zurich has introduced a new open-source quantization method for large language models (LLMs) aimed at reducing memory demands without sacrificing output quality.