Categories: AI/ML News

New training technique opens the door to neural networks that require much less energy

AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our brains. They are trained with vast quantities of data on high-performance computers, gobbling up massive amounts of energy in the process.

Optical neural networks hold promise for image processing

Cornell researchers have developed an optical neural network (ONN) that can filter relevant information from a scene before the visual image is detected by a camera, a method that may make it possible to build faster, smaller and more energy-efficient image sensors.

April 28, 2023

In "AI/ML News"

Researchers explore how to bring larger neural networks closer to the energy efficiency of biological brains

The more lottery tickets you buy, the higher your chances of winning, but spending more than you win is obviously not a wise strategy. Something similar happens in AI powered by deep learning: we know that the larger a neural network is (i.e., the more parameters it has), the better…

November 20, 2024

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Programming light propagation creates highly efficient neural networks

Current artificial intelligence models utilize billions of trainable parameters to achieve challenging tasks. However, this large number of parameters comes with a hefty cost. Training and deploying these huge models require immense memory space and computing capability that can only be provided by hangar-sized data centers in processes that consume…

January 26, 2024

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