Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers

Abstract

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 extremely low-bit compression, for example, binarized RNNLMs, are gaining increasing research interests. Directly training of quantized neural networks is difficult. By formulating quantized RNNLMs training as an optimization problem, this paper presents a novel method to train quantized RNNLMs from scratch using alternating direction methods of multipliers (ADMM). This method can also flexibly adjust the trade-off between the compression rate and model performance using tied low-bit quantization tables. Experiments on two tasks: Penn Treebank (PTB), and Switchboard (SWBD) suggest the proposed ADMM quantization achieved a model size compression factor of up to 31 times over the full precision baseline RNNLMs. Faster convergence of 5 times in model training over the baseline binarized RNNLM quantization was also obtained. Index Terms: Language models, Recurrent neural networks, Quantization, Alternating direction methods of multipliers.

References in corpus (10)

• Distilling the Knowledge in a Neural Network
• Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
• Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
• Compressing Deep Convolutional Networks using Vector Quantization
• Ternary Weight Networks
• Trained Ternary Quantization
• Compressing Neural Networks with the Hashing Trick
• Alternating Multi-bit Quantization for Recurrent Neural Networks
• Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
• Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation