A computer scientist has developed an AI-ready architecture that is twice as powerful as comparable in-memory computing approaches. The researcher applies a new computational paradigm using special circuits known as ferroelectric field effect transistors (FeFETs).
Within a few years, this could prove useful for generative AI, deep learning algorithms and robotic applications.Hussam Amrouch has developed an AI-ready architecture that is twice as powerful as comparable in-memory computing approaches. As reported in the journal Nature, the professor at the Technical University of Munich applies a new computational paradigm using special circuits known as ferroelectric field effect transistors .
The basic idea is simple: unlike previous chips, where only calculations were carried out on transistors, they are now the location of data storage as well. That saves time and energy."As a result, the performance of the chips is also boosted," says Hussam Amrouch, a professor of AI processor design at the Technical University of Munich .
AI is a powerful tool for studying the human genome. But its recent popularity has inundated the field with innovation. With so many options, it's hard to know which AI algorithms work best. ... Computer scientists developed a deep learning method to create realistic objects for virtual environments that can be used to train robots. The researchers used TACC's Maverick2 supercomputer to ...
Transistors have been miniaturized for the past 50 years, but we've reached the point where they can't continue to be scaled any further. Researchers review negative capacitance ...
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