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12 April 2021 Efficient normalization techniques to optimize AI models for deployment in tactical edge
Billy E. Geerhart III, Venkat R. Dasari, Peng Wang, David M. Alexander
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Proceedings Volume 11751, Disruptive Technologies in Information Sciences V; 117510F (2021) https://doi.org/10.1117/12.2585788
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
Normalizations used in model reduction can be chosen to emphasize anything from computation reduction to parameter reduction. Choosing a normalization that emphasizes a model with a small number of parameters is useful when deploying a model onto machines with a limited communication rate, while choosing a normalization that emphasizes a model with a small computational cost is useful when deploying a model onto a machine for real-time sensor analysis. As such, we explore the effect of various normalizations used to prune kernel parameters on models trained on the ImageNet database.
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Billy E. Geerhart III, Venkat R. Dasari, Peng Wang, and David M. Alexander "Efficient normalization techniques to optimize AI models for deployment in tactical edge", Proc. SPIE 11751, Disruptive Technologies in Information Sciences V, 117510F (12 April 2021); https://doi.org/10.1117/12.2585788
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KEYWORDS
Artificial intelligence
Data modeling
Databases
Sensors
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