Convolutional neural network libraries benchmarking
Felipe de Almeida Florencio, Edward David Moreno · 2020
Context: The growing use of Convolutional Neural Networks (CNNs) in scientific experiments and industry has resulted in the development of several Convolutional Neural Networks libraries. There is a need on the part of developers and scientists to identify scholarly works that evaluate the performance of these libraries on different computer architectures. Objective: Identify and systematize the CNN libraries benchmarkings. Method: A systematic literature mapping was conducted to analyze the scientific research in the field. Results: Systematic mapping found 12 papers that evaluate the performance of Convolutional Neural Networks on different computer architectures. Conclusion: The state of the art has been mapped, there are still few works that evaluate Convolutional Neural Networks libraries with statistical accuracy.