  We present in this paper an ultra-low power (ULP) Recurrent Neural Network
(RNN) based classifier for an always-on voice Wake-Up Sensor (WUS) with
performances suitable for real-world applications. The purpose of our sensor is
to bring down by at least a factor 100 the power consumption in background
noise of always-on speech processing algorithms such as Automatic Speech
Recognition, Keyword Spotting, Speaker Verification, etc. Unlike the other
published approaches, we designed our wake-up sensor to be robust to unseen
real-world noises for realistic levels of speech and noise by carefully
designing the dataset and the loss function. We also specifically trained it to
mark only the speech start rather than adopting a traditional Voice Activity
Detection (VAD) approach. We achieve less than 3% No Trigger Rate (NTR) for a
duty cycle less than 1% in challenging background noises pooled using a model
of an analogue front-end. We demonstrate the superiority of RNNs on this task
compared to the other tested approaches, with an estimated power consumption of
45 nW for the RNN itself in 65nm CMOS and a minimal memory footprint of 0.52
kB.
