Researchers have developed photonic computing chips that overcome key limitations for a type of neural network known as a photonic spiking neural system. By enabling fast learning and decision making ...

The growing energy use of AI has gotten a lot of people working on ways to make it less power hungry. One option is to develop processors that are a better match to the sort of computational needs of ...

Assistant Professor of Electrical and Computer Engineering Jason Eshraghian. Four years ago, UC Santa Cruz’s Jason Eshraghian developed a Python library that combines neuroscience with artificial ...

Photonic neural network systems, which are fast and energy efficient, are especially helpful for dealing with large amounts of data. To advance photonic brain-like computing technologies, a group of ...

(Nanowerk Spotlight) Effectively mimicking the unmatched visual capacities of the human brain while operating within stringent energy constraints poses a formidable challenge for artificial ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

The two-chip system includes a 16-channel photonic neuromorphic chip with 272 trainable parameters, giving it the ability to process multiple streams of optical signals at once and adjust many ...