Ultra-Low Power Keyword Spotting at the Edge
- URL: http://arxiv.org/abs/2111.04988v1
- Date: Tue, 9 Nov 2021 08:24:36 GMT
- Title: Ultra-Low Power Keyword Spotting at the Edge
- Authors: Mehmet Gorkem Ulkar, Osman Erman Okman
- Abstract summary: Keywords spotting (KWS) has become an indispensable part of many intelligent devices surrounding us.
In this work, we design an optimized KWS CNN model by considering end-to-end energy efficiency for the deployment at MAX78000.
With the combined hardware and model optimization approach, we achieve 96.3% accuracy for 12 classes while only consuming 251 uJ per inference.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Keyword spotting (KWS) has become an indispensable part of many intelligent
devices surrounding us, as audio is one of the most efficient ways of
interacting with these devices. The accuracy and performance of KWS solutions
have been the main focus of the researchers, and thanks to deep learning,
substantial progress has been made in this domain. However, as the use of KWS
spreads into IoT devices, energy efficiency becomes a very critical requirement
besides the performance. We believe KWS solutions that would seek power
optimization both in the hardware and the neural network (NN) model
architecture are advantageous over many solutions in the literature where
mostly the architecture side of the problem is considered. In this work, we
designed an optimized KWS CNN model by considering end-to-end energy efficiency
for the deployment at MAX78000, an ultra-low-power CNN accelerator. With the
combined hardware and model optimization approach, we achieve 96.3\% accuracy
for 12 classes while only consuming 251 uJ per inference. We compare our
results with other small-footprint neural network-based KWS solutions in the
literature. Additionally, we share the energy consumption of our model in
power-optimized ARM Cortex-M4F to depict the effectiveness of the chosen
hardware for the sake of clarity.
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