4 citations · 9 across the 5 of their papers we have counts for
5 papers
FCDP: Fully Cached Data Parallel for Communication-Avoiding Large-Scale Training
Gyeongseo Park, Eungyeong Lee, Song-woo Sok +5
Training billion-parameter models requires distributing model states across GPUs using fully sharded data parallel (i.e., ZeRO-3). While ZeRO-3 succeeds on clusters with high-bandw…
MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization
Han-Byul Kim, Joo Hyung Lee, Sungjoo Yoo +1
Mixed-precision quantization of efficient networks often suffer from activation instability encountered in the exploration of bit selections. To address this problem, we propose a…
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation
Inkyu Shin, Dahun Kim, Qihang Yu +6
Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-level scene understanding by segmenting all pixels and associating objects in a video. Current solutions can b…
TubeFormer-DeepLab: Video Mask Transformer
Dahun Kim, Jun Xie, Huiyu Wang +6
We present TubeFormer-DeepLab, the first attempt to tackle multiple core video segmentation tasks in a unified manner. Different video segmentation tasks (e.g., video semantic/inst…
NNStreamer: Stream Processing Paradigm for Neural Networks, Toward Efficient Development and Execution of On-Device AI Applications
MyungJoo Ham, Ji Joong Moon, Geunsik Lim +8
We propose nnstreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to neural network applications. A new…