13 citations · 30 across the 5 of their papers we have counts for
12 papers
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
Se Jung Kwon, Jeonghoon Kim, Jeongin Bae +7
There are growing interests in adapting large-scale language models using parameter-efficient fine-tuning methods. However, accelerating the model itself and achieving better infer…
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation
Seongmin Hong, Seungjae Moon, Junsoo Kim +4
Transformer is a deep learning language model widely used for natural language processing (NLP) services in datacenters. Among transformer models, Generative Pre-trained Transforme…
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…