28 citations · 144 across the 32 of their papers we have counts for
3 papers · 1 filter
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…