Tflms: Large model support in tensorflow by graph rewriting TD Le, H Imai, Y Negishi, K Kawachiya arXiv preprint arXiv:1807.02037, 2018 | 42 | 2018 |
Compiling onnx neural network models using mlir T Jin, GT Bercea, TD Le, T Chen, G Su, H Imai, Y Negishi, A Leu, ... arXiv preprint arXiv:2008.08272, 2020 | 39 | 2020 |
Automatic gpu memory management for large neural models in tensorflow TD Le, H Imai, Y Negishi, K Kawachiya Proceedings of the 2019 ACM SIGPLAN International Symposium on Memory …, 2019 | 20 | 2019 |
Efficient query evaluation on distributed graphs with Hadoop environment LD Tung, Q Nguyen-Van, Z Hu Proceedings of the 4th Symposium on Information and Communication Technology …, 2013 | 17 | 2013 |
Towards systematic parallelization of graph transformations over pregel LD Tung, Z Hu International Journal of Parallel Programming 45, 320-339, 2017 | 14 | 2017 |
Large model support for deep learning in caffe and chainer M Cho, TD Le, U Finkler, H Imai, Y Negishi, T Sekiyama, S Vinod, ... SysML, 2018 | 13 | 2018 |
Fast and accurate 3D medical image segmentation with data-swapping method H Imai, S Matzek, TD Le, Y Negishi, K Kawachiya arXiv preprint arXiv:1812.07816, 2018 | 12 | 2018 |
Minimizing data transfers for regular reachability queries on distributed graphs Q Nguyen-Van, LD Tung, Z Hu Proceedings of the 4th Symposium on Information and Communication Technology …, 2013 | 12 | 2013 |
Failure-aware Scheduling in Grid Computing Environments. T Do, T Nguyen, DT Nguyen, HC Nguyen, T Le GCA, 40-46, 2009 | 12 | 2009 |
Real-time resource usage reduction in artificial neural networks T Sekiyama, K Kawachiya, TD Le, Y Negishi US Patent 10,268,951, 2019 | 10 | 2019 |
Profiling based out-of-core hybrid method for large neural networks: poster Y Ito, H Imai, TL Duc, Y Negishi, K Kawachiya, R Matsumiya, T Endo Proceedings of the 24th Symposium on Principles and Practice of Parallel …, 2019 | 9 | 2019 |
Involving cpus into multi-gpu deep learning TD Le, T Sekiyama, Y Negishi, H Imai, K Kawachiya Proceedings of the 2018 ACM/SPEC international conference on performance …, 2018 | 9 | 2018 |
Multi-GPU deep learning using CPUs TD Le, H Imai, T Sekiyama, Y Negishi US Patent 11,164,079, 2021 | 7 | 2021 |
Pregel meets UnCAL: A systematic framework for transforming big graphs LD Tung 2015 31st IEEE International Conference on Data Engineering Workshops, 250-254, 2015 | 7 | 2015 |
Efficient parallel training of a network model on multiple graphics processing units I Haruki, TD Le, Y Negishi US Patent 10,949,746, 2021 | 6 | 2021 |
Localizing tree-based convolutional neural networks TD Le, T Sekiyama US Patent 11,106,970, 2021 | 5 | 2021 |
High resolution medical image segmentation using data-swapping method H Imai, S Matzek, TD Le, Y Negishi, K Kawachiya Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 4 | 2019 |
An Intermediate Library for Multi-GPUs Computing Skeletons T D. Le, NH Duc, PT Anh, NH Hoang, NM Thap hgpu. org, 2012 | 3 | 2012 |
Large data flow graphs in limited gpu memory G Janssen, V Zolotov, TD Le 2019 IEEE International Conference on Big Data (Big Data), 1821-1830, 2019 | 2 | 2019 |
Optimizing tree-based convolutional neural networks TD Le, T Sekiyama, K Zhao US Patent App. 15/903,600, 2018 | 1 | 2018 |