Abstract
Automated design methods for convolutional neural networks (CNNs) have recently been developed in order to increase the design productivity. We propose a neuroevolution method capable of evolving and optimizing CNNs with respect to the classification error and CNN complexity (expressed as the number of tunable CNN parameters), in which the inference phase can partly be executed using fixed point operations to further reduce power consumption. Experimental results are obtained with TinyDNN framework and presented using two common image classification benchmark problems – MNIST and CIFAR-10.
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Acknowledgments
This work was supported by the Ministry of Education, Youth and Sports, under the INTER-COST project LTC 18053, NPU II project IT4Innovations excellence in science LQ1602 and by Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center – LM2015070”.
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Badan, F., Sekanina, L. (2019). Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution. In: Martín-Vide, C., Pond, G., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2019. Lecture Notes in Computer Science(), vol 11934. Springer, Cham. https://doi.org/10.1007/978-3-030-34500-6_7
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DOI: https://doi.org/10.1007/978-3-030-34500-6_7
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