As inference workloads move into physical environments, latency becomes a liability, a variable that can’t be abstracted away ...

The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, where data ...

A collaborative research team led by Professor Pan Feng from the School of New Materials at Peking University Shenzhen ...

These results highlight the potential of the Autoencoder–GCN pipeline as a scalable and reliable solution for AMS structure recognition under real-world constraints.

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light instead of electricity to process information—promise faster speeds ...

Neural collapse (NC) reveals that the last layer of the network can capture data representations, leading to similar outputs for examples within the same class, while outputs for examples from ...

In Ukraine, memories of Russia’s annexation are fresh and resentments run high, leaving the country’s president few choices on the latest American peace plan.

Delaware-based TheStage AI is changing this paradigm with their innovative approach to neural network optimization. The startup recently announced a $4.5 million funding round to commercialize ...

ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...

This article presents a complete demo of neural network quantile regression using the C# language. To the best of my knowledge, there are no existing code libraries that directly implement neural ...