Precision Highway for Ultra Low-Precision Quantization

2018/12/24 by Eunhyeok Park, Dongyoung Kim, Park, Eunhyeok +5 · 1 citation
Computer Science · Physics and Astronomy · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Model Reduction and Neural Networks #Multimodal Machine Learning Applications

paper · pdf · doi:10.48550/arxiv.1812.09818

openalex publication_date 2018/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Neural network quantization has an inherent problem called accumulated quantization error, which is the key obstacle towards ultra-low precision, e.g., 2- or 3-bit precision. To resolve this problem, we propose precision highway, which forms an end-to-end high-precision information flow while performing the ultra low-precision computation. First, we describe how the precision highway reduce the accumulated quantization error in both convolutional and recurrent neural networks. We also provide the quantitative analysis of the benefit of precision highway and evaluate the overhead on the state-of-the-art hardware accelerator. In the experiments, our proposed method outperforms the best existing quantization methods while offering 3-bit weight/activation quantization with no accuracy loss and 2-bit quantization with a 2.45 % top-1 accuracy loss in ResNet-50. We also report that the proposed method significantly outperforms the existing method in the 2-bit quantization of an LSTM for language modeling.

Citations

• Recurrent Neural Network Regularization
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Learning to Forget: Continual Prediction with LSTM
• DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
• Trained Ternary Quantization
• Bit Fusion: Bit-Level Dynamically Composable Architecture for\n Accelerating Deep Neural Networks
• Visualizing the Loss Landscape of Neural Nets
• Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm

Cited by

• Confounding Tradeoffs for Neural Network Quantization

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