A Pre-Training Pruning Strategy for Enabling Lightweight Non-Intrusive Load Monitoring On Edge Devices

A novel pre-training Deep Neural Network (DNN) model compression strategy within the context of Non-Intrusive Load Monitoring (NILM) is presented. Our approach leverages an iterative magnitude pruning technique based on L 1 norms to identify an optimal, compressed in terms of trainable parameters DN...

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Bibliographic Details
Published in2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) pp. 249 - 253
Main Authors Athanasoulias, Sotirios, Sykiotis, Stavros, Temenos, Nikos, Doulamis, Anastasios, Doulamis, Nikolaos
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.04.2024
Subjects
Artificial neural networks
Compression
Computational efficiency
Computational modeling
Energy Disaggregation
Load monitoring
Non-Intrusive Load Monitoring
Optimization
Pruning
Signal processing
Speech processing
Training
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Summary:A novel pre-training Deep Neural Network (DNN) model compression strategy within the context of Non-Intrusive Load Monitoring (NILM) is presented. Our approach leverages an iterative magnitude pruning technique based on L 1 norms to identify an optimal, compressed in terms of trainable parameters DNN that balances the model's computational complexity, translated as number of Multiply-and-Accumulate units, with respect to the model's performance. By doing so, in the training phase, a smaller DNN will be used, resulting in reduced computational cost up to 95%, while at the same time having a negligible performance degradation, in contrast to existing NILM post-training compression schemes. Experimental results on the UK-DALE dataset demonstrate the proposed strategy's efficacy, isolating sub-networks using only 5% of the original model's parameters, achieving comparable performance to the initial model. These features make the proposed strategy suitable for edge IoT deployment in real-world scenarios.
DOI:10.1109/ICASSPW62465.2024.10626463