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Computational Complexity Evaluation of Neural Network Applications in Signal Processing
March 12, 2024, 4:44 a.m. | Pedro Freire, Sasipim Srivallapanondh, Antonio Napoli, Jaroslaw E. Prilepsky, Sergei K. Turitsyn
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we provide a systematic approach for assessing and comparing the computational complexity of neural network layers in digital signal processing. We provide and link four software-to-hardware complexity measures, defining how the different complexity metrics relate to the layers' hyper-parameters. This paper explains how to compute these four metrics for feed-forward and recurrent layers, and defines in which case we ought to use a particular metric depending on whether we characterize a more …
abstract applications arxiv complexity computational cs.cc cs.lg digital eess.sp evaluation hardware metrics network neural network paper parameters processing signal software type
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