Abstract
Considering the power-hungry nature of speech processing, a keyword spotting (KWS) unit, used to detect multiple spoken words, is often integrated as a front-end layer. KWS systems are always active, and thus, it is extremely important to optimize the devoted power budget. In this context, this article presents a programmable low-power event-driven real-time spectrogram extraction unit tested for the KWS application. This chip, fabricated in 28-nm FD-SOI CMOS technology, has been combined with a software-defined convolutional neural network to demonstrate the recognition of 11 audio classes (ten keywords $+$ background $+$ unknown) with an accuracy equal to 87.9% and an activity-dependent power consumption measured at 391.6 nW, for a 12-keyword/min average speech rate.

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