Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation

26 August 2022

Jaroslaw E. Prilepsky

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Papers citing "Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation"

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Title
Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks Dominika Przewlocka-Rus Syed Shakib Sarwar H. Sumbul Yuecheng Li B. D. Salvo MQ 53 30 0 09 Mar 2022
Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems Pedro J. Freire B. Spinnler Daniel Abode Jaroslaw E. Prilepsky Abdallah A. i. Ali N. Costa W. Schairer A. Napoli A. Ellis S. Turitsyn OOD 40 14 0 25 Feb 2022
Neural Network Quantization for Efficient Inference: A Survey Olivia Weng MQ 33 23 0 08 Dec 2021
Performance and Complexity Analysis of bi-directional Recurrent Neural Network Models vs. Volterra Nonlinear Equalizers in Digital Coherent Systems S. Deligiannidis C. Mesaritakis Adonis Bogris 33 45 0 03 Mar 2021
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms Rishabh Goyal Joaquin Vanschoren V. V. Acht S. Nijssen MQ 48 23 0 03 Feb 2021
Physics-Based Deep Learning for Fiber-Optic Communication Systems Christian Hager H. Pfister 44 68 0 27 Oct 2020
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors C. Coelho Aki Kuusela Shane Li Zhuang Hao T. Aarrestad Vladimir Loncar J. Ngadiuba M. Pierini Adrian Alan Pol S. Summers MQ 50 178 0 15 Jun 2020
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 236 1,040 0 06 Mar 2020
Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks S. Deligiannidis Adonis Bogris C. Mesaritakis Y. Kopsinis 18 69 0 31 Jan 2020
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks Yuhang Li Xin Dong Wei Wang MQ 40 255 0 28 Sep 2019
DeepShift: Towards Multiplication-Less Neural Networks Mostafa Elhoushi Zihao Chen F. Shafiq Ye Tian Joey Yiwei Li MQ 56 97 0 30 May 2019
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning Zechun Liu Haoyuan Mu Xiangyu Zhang Zichao Guo Xin Yang K. Cheng Jian Sun 48 557 0 25 Mar 2019
Towards Hardware Implementation of Neural Network-based Communication Algorithms Fayçal Ait Aoudia J. Hoydis 32 11 0 19 Feb 2019
A Tutorial on Bayesian Optimization P. Frazier GP 73 1,759 0 08 Jul 2018
Deep $k$ -Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions Junru Wu Yue Wang Zhenyu Wu Zhangyang Wang Ashok Veeraraghavan Yingyan Lin 30 115 0 24 Jun 2018
AMC: AutoML for Model Compression and Acceleration on Mobile Devices Yihui He Ji Lin Zhijian Liu Hanrui Wang Li Li Song Han 60 1,346 0 10 Feb 2018
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference Benoit Jacob S. Kligys Bo Chen Menglong Zhu Matthew Tang Andrew G. Howard Hartwig Adam Dmitry Kalenichenko MQ 110 3,090 0 15 Dec 2017
To prune, or not to prune: exploring the efficacy of pruning for model compression Michael Zhu Suyog Gupta 106 1,262 0 05 Oct 2017
Training Quantized Nets: A Deeper Understanding Hao Li Soham De Zheng Xu Christoph Studer H. Samet Tom Goldstein MQ 35 210 0 07 Jun 2017
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 172 8,793 0 01 Oct 2015
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 249 7,883 0 13 Jun 2012