Temperature-independent clock generation systems and methods are described that include a trained neural network coupled to a frequency correction circuit that corrects a crystal resonator output of a clock signal having a frequency that changes with changes in temperature. The neural network is trained with test temperatures and corresponding temperature based changes in frequency for test resonators of the same type as the resonator of the real time clock. The neutral network is trained to output frequency corrections based on a set of measured reference temperature-based changes in frequency for the crystal resonator and a current temperature of the resonator. The frequency correction circuit receives the frequency corrections from the neural network and corrects changes in the frequency caused by the changes in temperature of the resonator to provide a clock signal having an output frequency that is independent of the current temperature of the resonator.
They do use LSTM in speech recognition/key word spotting:
Automatic speech recognition (ASR) systems and methods. An ASR system includes a vector memory to store a plurality of feature vectors sequentially extracted from an audio data stream. A first neural network performs speech recognition processing on feature vectors stored in the vector memory to attempt to recognize a word from a predetermined vocabulary. A second neural network controls when the first neural network performs the speech recognition processing. The first neural network is held in a quiescent state except when performing the speech recognition processing under control of the second neural network.
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