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4. Low-Bit Quantization Of Recurrent Neural Network Language Models Using Alternating Direction Methods Of Multipliers

Low-Bit Quantization Of Recurrent Neural Network Language Models Using Alternating Direction Methods Of Multipliers

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IEEE ICASSP 2020 Virtual Conference May 2020

The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent years, neural network quantization techniques that are capable of producing e

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Low-Bit Quantization Of Recurrent Neural Network Language Models Using Alternating Direction Methods Of Multipliers

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The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent years, neural network quantization techniques that are capable of producing e