Low power analog circuitry for artificial neural networks

Abstract

A low power analog Long Short-Term Memory (LSTM) recurrent neural network has an input layer, an array of Adaptive Filter Unit for Analog LSTM, a linear projection layer, and an output layer. The output layer has multiple nonlinear amplifiers, a nonlinear element with a sigmoidal input-output characteristic function, and a time-constant adjustable, nonlinear, low pass filter that provides the memory function of the LSTM. The LSTM memory is used with mismatch-robust weights determined by learning by computation of optimal weights values, wherein the objective function minimizes misdetection probability, and used to process a signal to detect events.

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