Neural architecture search for energy-efficient always-on audio machine learning

Mobile and edge computing devices for always-on classification tasks require energy-efficient neural network architectures. In this paper we present several changes to neural architecture searches that improve the chance of success in practical situations. Our search simultaneously optimizes for net...

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Bibliographic Details
Published in:Neural computing & applications 2023-06, Vol.35 (16), p.12133-12144
Main Authors: Speckhard, Daniel T., Misiunas, Karolis, Perel, Sagi, Zhu, Tenghui, Carlile, Simon, Slaney, Malcolm
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer architecture
Computer Science
Data Mining and Knowledge Discovery
Edge computing
Energy consumption
Energy efficiency
Hardware
Image Processing and Computer Vision
Machine learning
Mobile computing
Neural networks
Original Article
Probability and Statistics in Computer Science
Search methods
Sound filters
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Summary:Mobile and edge computing devices for always-on classification tasks require energy-efficient neural network architectures. In this paper we present several changes to neural architecture searches that improve the chance of success in practical situations. Our search simultaneously optimizes for network accuracy, energy efficiency and memory usage. We benchmark the performance of our search on real hardware, but since running thousands of tests with real hardware is difficult, we use a random forest model to roughly predict the energy usage of a candidate network. We present a search strategy that uses both Bayesian and regularized evolutionary search with particle swarms, and employs early stopping to reduce the computational burden. Our search, evaluated on a sound event classification dataset based upon AudioSet, results in an order of magnitude less energy per inference and a much smaller memory footprint than our baseline MobileNetV1/V2 implementations while slightly improving task accuracy. We also demonstrate how combining a 2D spectrogram with a convolution with many filters causes a computational bottleneck for audio classification and that alternative approaches reduce the computational burden but sacrifice task accuracy.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08345-y