Over the last several years we have seen many new hardware architectures emerge for deep learning training but this year, inference will have its turn in the spotlight. For those chips that can manage ...

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 ...

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 ...

Neural networks rank as the hottest machine-learning (ML) trends in artificial-intelligence work these days. Actually, many different forms and implementations of neural networks exist, with ...

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 ...

A two-chip photonic neuromorphic system performs real time spiking reinforcement learning using only light, achieving GPU-class energy efficiency. (Nanowerk News) A research team based at Xidian ...

A new publication from Opto-Electronic Advances, 10.29026/oea.2023.230140 discusses photonic integrated neuro-synaptic core for convolutional spiking neural network. Brain science and brain-like ...

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