Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and
Binary Neural Networks
- URL: http://arxiv.org/abs/2201.03386v1
- Date: Mon, 10 Jan 2022 15:10:58 GMT
- Title: Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and
Binary Neural Networks
- Authors: Gianmarco Cerutti, Lukas Cavigelli, Renzo Andri, Michele Magno,
Elisabetta Farella, Luca Benini
- Abstract summary: Keywords spotting (KWS) is a crucial function enabling the interaction with the many ubiquitous smart devices in our surroundings.
This work addresses KWS energy-efficiency on low-cost microcontroller units (MCUs)
By replacing the digital preprocessing with the proposed analog front-end, we show that the energy required for data acquisition and preprocessing can be reduced by 29x.
- Score: 19.40893986868577
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Keyword spotting (KWS) is a crucial function enabling the interaction with
the many ubiquitous smart devices in our surroundings, either activating them
through wake-word or directly as a human-computer interface. For many
applications, KWS is the entry point for our interactions with the device and,
thus, an always-on workload. Many smart devices are mobile and their battery
lifetime is heavily impacted by continuously running services. KWS and similar
always-on services are thus the focus when optimizing the overall power
consumption. This work addresses KWS energy-efficiency on low-cost
microcontroller units (MCUs). We combine analog binary feature extraction with
binary neural networks. By replacing the digital preprocessing with the
proposed analog front-end, we show that the energy required for data
acquisition and preprocessing can be reduced by 29x, cutting its share from a
dominating 85% to a mere 16% of the overall energy consumption for our
reference KWS application. Experimental evaluations on the Speech Commands
Dataset show that the proposed system outperforms state-of-the-art accuracy and
energy efficiency, respectively, by 1% and 4.3x on a 10-class dataset while
providing a compelling accuracy-energy trade-off including a 2% accuracy drop
for a 71x energy reduction.
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