no code implementations • 16 Jan 2021 • MyungJoo Ham, Jijoong Moon, Geunsik Lim, Jaeyun Jung, Hyoungjoo Ahn, Wook Song, Sangjung Woo, Parichay Kapoor, Dongju Chae, Gichan Jang, Yongjoo Ahn, Jihoon Lee
NNStreamer efficiently handles neural networks with complex data stream pipelines on devices, significantly improving the overall performance with minimal efforts.
no code implementations • 25 Sep 2019 • Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Parichay Kapoor, Gu-Yeon Wei
In this paper, we propose a new network pruning technique that generates a low-rank binary index matrix to compress index data significantly.
no code implementations • CVPR 2020 • Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, Gu-Yeon Wei
Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations.
no code implementations • 14 May 2019 • Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Parichay Kapoor, Gu-Yeon Wei
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs).
no code implementations • 30 Oct 2018 • Dongsoo Lee, Parichay Kapoor, Byeongwook Kim
Model compression has been introduced to reduce the required hardware resources while maintaining the model accuracy.
no code implementations • 27 Sep 2018 • Parichay Kapoor, Dongsoo Lee, Byeongwook Kim, Saehyung Lee
We present a non-intrusive quantization technique based on re-training the full precision model, followed by directly optimizing the corresponding binary model.
no code implementations • 3 Feb 2017 • James R. Geraci, Parichay Kapoor
Convolutional Neural Network (CNN) recognition rates drop in the presence of noise.