In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. The use of sparsity and pruning for optimizing ...

Spiking Neural Networks (SNNs) are often regarded as the third generation of Artificial Neural Networks (ANNs) because their functionality closely resembles that of the mammalian brain compared to ...

As artificial intelligence systems grow larger and more powerful, their energy demands are rising dramatically. But recent ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

Researchers in Sweden have developed a machine-learning approach that embeds the laws of physics directly into neural ...

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...

Designing materials that steer light is a slow kind of trial and error. Each candidate structure must be tested in computer ...

Artificial Deep Neural Networks (DNNs) and, more recently, large-scale foundation models have made breakthrough progress drawing loose structural and ...

The Power of Attention Networks: Dr. Feng isolated baseline connectivity within attention and cognitive control networks as ...

Biological cells process data and perform computations all the time. They take inputs in the form of external stimuli and produce specific responses. Recently, scientists have been looking at ways to ...

As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...