Neuromorphic chips more energy efficient for deep learning

#artificialintelligence 

Neuromorphic chips have been endorsed in research showing that they are much more energy efficient at operating large deep learning networks than non-neuromorphic hardware. This may become important as AI adoption increases. The study was carried out by the Institute of Theoretical Computer Science at the Graz University of Technology (TU Graz) in Austria using Intel's Loihi 2 silicon, a second-generation experimental neuromorphic chip announced by Intel Labs last year that has about a million artificial neurons. Their research paper, "A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware," published in Nature Machine Intelligence, claims that the Intel chips are up to 16 times more energy efficient in deep learning tasks than performing the same task on non-neuromorphic hardware. The hardware tested consisted of 32 Loihi chips.

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