Trainable Analog Hardware Platform for Recurrent Neural Networks (RNN)

Machine learning models currently require extensive computational resources and this demand is growing rapidly with new models and applications being introduced. Addressing this need, Stanford researchers at the Fan Group have developed a specialized analog computing hardware platform for machine learning models such as RNN. This is an entirely analog method, using wave physics in a material, to achieve the behavior of a neural network. Benefits of this approach include improved computational performance, compact size, and higher energy efficiency, which enable high-speed neural network processing for extraction of patterns from large datasets. This invention has broad applications including, optics, audio / acoustics, medicine, edge computing, IoT, biology, finance, and speech recognition. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.

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