Performance and Complexity Analysis of Bi-Directional Recurrent Neural Network Models Versus Volterra Nonlinear Equalizers in Digital Coherent Systems

Deligiannidis, Stavros; Mesaritakis, Charis; Bogris, Adonis

doi:10.1109/jlt.2021.3092415

Cited by 57 publications

(49 citation statements)

References 25 publications

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“…It should be stressed that in the telecommunication industry, the competition between possible solutions occurs not only in terms of performance but also in terms of hardware deployment options, operational costs, and power consumption. During the last years, the approaches based on machine learning techniques and, in particular, those utilizing NNs, have become an increasingly popular topic of research, as they can efficiently unroll both fiber and component-induced impairments [5][6][7][8][9][10][11][12][13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

“…A number of NNs architectures have been already studied in different types of optical systems (submarine, long-haul, metro, and access). These architectures include the feed-forward designs such as the MLP 7,10,14,15 , considered in the current study, or more sophisticated recurrent-type NN structures [10][11][12]17 . The predominant number of preceding studies have demonstrated the potential of using the NNs in the optical channel equalization task, especially in terms of the transmission quality improvement 7,8 .…”

Section: Introductionmentioning

confidence: 99%

“…A high computational complexity leads to high memory and computing power requirements, increasing the energy and resource consumption 19,20 . Thus, the use of NN-based methods, while being, undoubtedly, promising and attractive, faces a major challenge in optical channel equalization, where the computational complexity emerges as an important limiting real-time deployment factor 10,12,20,21 . We note here that it is, of course, well known that some NN architectures can be simplified without significantly affecting their performance, thanks, e.g., to strategies such as pruning and quantization 19,20,[22][23][24][25] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization

Costa

Sourza

Prilepsky

et al. 2022

Preprint

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The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while keeping an acceptable performance level. In this work, we address this problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), and address its complexity reduction for the 30 GBd 1000 km transmission over a standard single-mode fiber. We demonstrate that it is feasible to reduce the equalizer’s memory by up to 87.12%, and its complexity by up to 91.5%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense and examine the impact of using different CPU and GPU settings on power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, using two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization

Costa

Sourza

Prilepsky

et al. 2022

Preprint

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show abstract

“…A number of NN architectures have already been analyzed in different types of optical systems (submarine, long-haul, metro, and access). These architectures include the feed-forward NN designs such as the MLP 7 , 10 , 14 , 15 , considered in the current study, or more sophisticated recurrent-type NN structures 10 – 12 , 17 . However, the practical deployment of real-time NN-based channel equalizers implies that their computational complexity is, at least, comparable, or, desirably lower than that of existing conventional digital signal processing (DSP) solutions 18 , and remains a matter of debate.…”

Section: Introductionmentioning

confidence: 99%

“…The high computational complexity leads, in turn, to high memory and computing power requirements, increasing the energy and resource consumption 19 , 20 . Thus, the use of NN-based methods, while being, undoubtedly, promising and attractive, faces a major challenge in optical channel equalization, where the computational complexity emerges as an important limiting real-time deployment factor 10 , 12 , 20 , 21 . We notice here that it is, of course, well known that some NN architectures can be simplified without significantly affecting their performance, thanks, e.g., to strategies such as pruning and quantization 19 , 20 , 22 – 25 .…”

Section: Introductionmentioning

confidence: 99%

Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization

Costa

Freire

Prilepsky

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is still a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large dispersion-induced memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while we have to keep an acceptable performance level of the simplified NN model. In this work, we address the complexity reduction problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), to mitigate the impairments for 30 GBd 1000 km transmission over a standard single-mode fiber, and demonstrate that it is feasible to reduce the equalizer’s memory by up to 87.12%, and its complexity by up to 78.34%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense. Further, we examine the impact of using hardware with different CPU and GPU features on the power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, by implementing the reduced NN equalizer on two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano, which are used to process the data generated via simulating the signal’s propagation down the optical-fiber system.

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Efficient Deep Learning of Kerr Nonlinearity in Fiber-Optic Channels Using a Convolutional Recurrent Neural Network

Shahkarami

Yousefi

Jaouën

2022

Deep Learning Applications, Volume 4

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Performance and Complexity Analysis of Bi-Directional Recurrent Neural Network Models Versus Volterra Nonlinear Equalizers in Digital Coherent Systems

Cited by 57 publications

References 25 publications

Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization

Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization

Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization

Efficient Deep Learning of Kerr Nonlinearity in Fiber-Optic Channels Using a Convolutional Recurrent Neural Network

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