6 citations · 6 across the 1 of their papers we have counts for
5 papers
Modulating Regularization Frequency for Efficient Compression-Aware Model Training
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +3
While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and…
Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization
Byeongwook Kim, Dongsoo Lee, Yeonju Ro +4
Various post-training uniform quantization methods have usually been studied based on convex optimization. As a result, most previous ones rely on the quantization error minimizati…
FleXOR: Trainable Fractional Quantization
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +3
Quantization based on the binary codes is gaining attention because each quantized bit can be directly utilized for computations without dequantization using look-up tables. Previo…
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation
Insoo Chung, Byeongwook Kim, Yoonjung Choi +5
The deployment of widely used Transformer architecture is challenging because of heavy computation load and memory overhead during inference, especially when the target device is l…
BiQGEMM: Matrix Multiplication with Lookup Table For Binary-Coding-based Quantized DNNs
Yongkweon Jeon, Baeseong Park, Se Jung Kwon +3
The number of parameters in deep neural networks (DNNs) is rapidly increasing to support complicated tasks and to improve model accuracy. Correspondingly, the amount of computation…