From 3bea4d0039ef73b3a5725cef461113e43a78ac61 Mon Sep 17 00:00:00 2001 From: Eunju Yang Date: Tue, 18 Jun 2024 13:03:43 +0900 Subject: [PATCH 1/3] [Docs] add recent proceeding to main README.md - This commit updates `README.md` to include recent publication and its citation. Signed-off-by: Eunju Yang --- README.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/README.md b/README.md index 8d80ffc18d..f6db3eceff 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ NNtrainer is an Open Source Project. The aim of the NNtrainer is to develop a So Even if NNtrainer runs on device, it provides full functionalities to train models and also utilizes limited device resources efficiently. NNTrainer is able to train various machine learning algorithms such as k-Nearest Neighbor (k-NN), Neural Networks, Logistic Regression, Reinforcement Learning algorithms, Recurrent network and more. We also provide examples for various tasks such as Few-shot learning, ResNet, VGG, Product Rating and more will be added. All of these were tested on Samsung Galaxy smart phone with Android and PC (Ubuntu 18.04/20.04). +[ A New Frontier of AI: On-Device AI Training and Personalization ](https://dl.acm.org/doi/abs/10.1145/3639477.3639716), ICSE-SEIP, 2024
[ NNTrainer: Light-Weight On-Device Training Framework ](https://arxiv.org/pdf/2206.04688.pdf), arXiv, 2022
[ NNTrainer: Towards the on-device learning for personalization ](https://www.youtube.com/watch?v=HWiV7WbIM3E), Samsung Software Developer Conference 2021 (Korean)
[ NNTrainer: Personalize neural networks on devices! ](https://www.youtube.com/watch?v=HKKowY78P1A), Samsung Developer Conference 2021
@@ -210,3 +211,25 @@ The NNtrainer is an open source project released under the terms of the Apache L Contributions are welcome! Please see our [Contributing](https://github.com/nnstreamer/nntrainer/blob/main/docs/contributing.md) Guide for more details. [![](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/images/0)](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/links/0)[![](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/images/1)](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/links/1)[![](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/images/2)](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/links/2)[![](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/images/3)](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/links/3)[![](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/images/4)](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/links/4)[![](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/images/5)](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/links/5)[![](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/images/6)](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/links/6)[![](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/images/7)](https://sourcerer.io/fame/dongju-chae/nnstreamer/nntrainer/links/7) + +## Citation + +If you find this NNTrainer project useful or relevant to your research, please consider citing our paper: + + +``` +@inproceedings{10.1145/3639477.3639716, +author = {Moon, Jijoong and Lee, Hyeonseok and Chu, Jiho and Park, Donghak and Hong, Seungbaek and Seo, Hyungjun and Jeong, Donghyeon and Kong, Sungsik and Ham, Myungjoo}, +title = {A New Frontier of AI: On-Device AI Training and Personalization}, +year = {2024}, +isbn = {9798400705014}, +publisher = {Association for Computing Machinery}, +url = {https://doi.org/10.1145/3639477.3639716}, +doi = {10.1145/3639477.3639716}, +booktitle = {Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice}, +pages = {323–333}, +numpages = {11}, +keywords = {on-device AI, neural network, personalization, training, software framework}, +series = {ICSE-SEIP '24} +} +``` From 2088864c92aad1413475de7b797e029baead5602 Mon Sep 17 00:00:00 2001 From: Eunju Yang Date: Tue, 18 Jun 2024 13:06:05 +0900 Subject: [PATCH 2/3] [Docs/trivial] fix typo in main `README.md` - This commit fixes typo in README.md Signed-off-by: Eunju Yang --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f6db3eceff..48e114514e 100644 --- a/README.md +++ b/README.md @@ -200,7 +200,7 @@ Currently, we provide [C APIs](https://github.com/nnstreamer/nntrainer/blob/mast * [Seungbaek Hong](https://github.com/baek2sm) * [Sungsik Kong](https://github.com/skykongkong8) * [Donghyeon Jeong](https://github.com/djeong20) -* [Eunyu Yang](https://github.com/EunjuYang) +* [Eunju Yang](https://github.com/EunjuYang) ## Open Source License From 05c39814205cb9a814e060411ef002dd3a7507e4 Mon Sep 17 00:00:00 2001 From: "Jubilee.Yang" Date: Thu, 20 Jun 2024 09:31:14 +0900 Subject: [PATCH 3/3] [DOCS] Update README.md - Update out-dated link with the recent one. Co-authored-by: Donghyeon Jeong <54725479+djeong20@users.noreply.github.com> Signed-off-by: Donghyeon Jeong --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 48e114514e..724078e8aa 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ Even if NNtrainer runs on device, it provides full functionalities to train mode [ A New Frontier of AI: On-Device AI Training and Personalization ](https://dl.acm.org/doi/abs/10.1145/3639477.3639716), ICSE-SEIP, 2024
[ NNTrainer: Light-Weight On-Device Training Framework ](https://arxiv.org/pdf/2206.04688.pdf), arXiv, 2022
-[ NNTrainer: Towards the on-device learning for personalization ](https://www.youtube.com/watch?v=HWiV7WbIM3E), Samsung Software Developer Conference 2021 (Korean)
+[ Open Source On-Device AI SW Platform ](https://youtu.be/im3uNrPLYx4?si=gMbw7LKKSnpXi59U), Samsung Developer Conference 2023 (Korean)
[ NNTrainer: Personalize neural networks on devices! ](https://www.youtube.com/watch?v=HKKowY78P1A), Samsung Developer Conference 2021
[ NNTrainer: "On-device learning" ](https://www.youtube.com/embed/Jy_auavraKg?start=4035&end=4080), Samsung AI Forum 2021