NNtrainer

NNtrainer is a Software Framework for training Neural Network models on devices.

Overview

NNtrainer is an Open Source Project. The aim of the NNtrainer is to develop a Software Framework to train neural network models on embedded devices which have relatively limited resources. Rather than training whole layers of a network from the scratch, NNtrainer finetunes the neural network model on device with user data for the personalization.

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 , ICSE-SEIP, 2024
NNTrainer: Light-Weight On-Device Training Framework , arXiv, 2022
Open Source On-Device AI SW Platform , Samsung Developer Conference 2023 (Korean)
NNTrainer: Personalize neural networks on devices! , Samsung Developer Conference 2021
NNTrainer: "On-device learning" , Samsung AI Forum 2021

Official Releases

	Tizen	Ubuntu	Android/NDK Build
	6.0M2 and later	18.04	9/P
arm		Available	Ready
arm64		Available
x64			Ready
x86		N/A	N/A
Publish	Tizen Repo	PPA
API	C (Official)	C/C++	C/C++

• Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
• Available: binary packages are released and deployed automatically and periodically along with CI tests.
• Daily Release
• SDK Support: Tizen Studio (6.0 M2+)

Getting Started

Installation

Instructions for installing NNTrainer.

Tutorial

Introductions for creating your own model.

Running Examples

Instructions for preparing NNTrainer for execution

Examples for NNTrainer

NNTrainer examples for a variety of networks

Components

Supported Layers

This component defines layers which consist of a neural network model. Layers have their own properties to be set.

Keyword	Layer Class Name	Description
conv1d	Conv1DLayer	Convolution 1-Dimentional Layer
conv2d	Conv2DLayer	Convolution 2-Dimentional Layer
pooling2d	Pooling2DLayer	Pooling 2-Dimentional Layer. Support average / max / global average / global max pooling
flatten	FlattenLayer	Flatten layer
fully_connected	FullyConnectedLayer	Fully connected layer
input	InputLayer	Input Layer. This is not always required.
batch_normalization	BatchNormalizationLayer	Batch normalization layer
layer_normalization	LayerNormalizationLayer	Layer normalization layer
activation	ActivationLayer	Set by layer property
addition	AdditionLayer	Add input input layers
attention	AttentionLayer	Attenstion layer
centroid_knn	CentroidKNN	Centroid K-nearest neighbor layer
concat	ConcatLayer	Concatenate input layers
multiout	MultiOutLayer	Multi-Output Layer
backbone_nnstreamer	NNStreamerLayer	Encapsulate NNStreamer layer
backbone_tflite	TfLiteLayer	Encapsulate tflite as a layer
permute	PermuteLayer	Permute layer for transpose
preprocess_flip	PreprocessFlipLayer	Preprocess random flip layer
preprocess_l2norm	PreprocessL2NormLayer	Preprocess simple l2norm layer to normalize
preprocess_translate	PreprocessTranslateLayer	Preprocess translate layer
reshape	ReshapeLayer	Reshape tensor dimension layer
split	SplitLayer	Split layer
dropout	DropOutLayer	Dropout Layer
embedding	EmbeddingLayer	Embedding Layer
positional_encoding	PositionalEncodingLayer	Positional Encoding Layer
rnn	RNNLayer	Recurrent Layer
rnncell	RNNCellLayer	Recurrent Cell Layer
gru	GRULayer	Gated Recurrent Unit Layer
grucell	GRUCellLayer	Gated Recurrent Unit Cell Layer
lstm	LSTMLayer	Long Short-Term Memory Layer
lstmcell	LSTMCellLayer	Long Short-Term Memory Cell Layer
zoneoutlstmcell	ZoneoutLSTMCellLayer	Zoneout Long Short-Term Memory Cell Layer
time_dist	TimeDistLayer	Time distributed Layer
multi_head_attention	MultiHeadAttentionLayer	Multi Head Attention Layer

Supported Optimizers

NNTrainer Provides

Keyword	Optimizer Name	Description
sgd	Stochastic Gradient Decent	-
adam	Adaptive Moment Estimation	-

Keyword	Learning Rate	Description
exponential	exponential learning rate decay	-
constant	constant learning rate	-
step	step learning rate	-

Supported Loss Functions

NNTrainer provides

Keyword	Class Name	Description
cross_sigmoid	CrossEntropySigmoidLossLayer	Cross entropy sigmoid loss layer
cross_softmax	CrossEntropySoftmaxLossLayer	Cross entropy softmax loss layer
constant_derivative	ConstantDerivativeLossLayer	Constant derivative loss layer
mse	MSELossLayer	Mean square error loss layer
kld	KLDLossLayer	Kullback-Leibler Divergence loss layer

Supported Activation Functions

NNTrainer provides

Keyword	Loss Name	Description
tanh	tanh function	set as layer property
sigmoid	sigmoid function	set as layer property
softmax	softmax function	set as layer property
relu	relu function	set as layer property
leaky_relu	leaky_relu function	set as layer property
swish	swish function	set as layer property
gelu	gelu function	set as layer property
quick_gelu	quick gelu function	set as layer property
elu	elu function	set as layer property
selu	selu function	set as layer property
softplus	softplus function	set as layer property
mish	mish function	set as layer property

Tensor

Tensor is responsible for calculation of a layer. It executes several operations such as addition, division, multiplication, dot production, data averaging and so on. In order to accelerate calculation speed, CBLAS (C-Basic Linear Algebra: CPU) and CUBLAS (CUDA: Basic Linear Algebra) for PC (Especially NVIDIA GPU) are implemented for some of the operations. Later, these calculations will be optimized. Currently, we support lazy calculation mode to reduce complexity for copying tensors during calculations.

Keyword	Description
4D Tensor	B, C, H, W
Add/sub/mul/div	-
sum, average, argmax	-
Dot, Transpose	-
normalization, standardization	-
save, read	-

Others

NNTrainer provides

Keyword	Loss Name	Description
weight_initializer	Weight Initialization	Xavier(Normal/Uniform), LeCun(Normal/Uniform), HE(Normal/Uniform)
weight_regularizer	weight decay ( L2Norm only )	needs set weight_regularizer_param & type

APIs

Currently, we provide C APIs for Tizen. C++ APIs are also provided for other platform. Java & C# APIs will be provided soon.

Maintainer

• Jijoong Moon
• MyungJoo Ham
• Geunsik Lim

Reviewers

• Sangjung Woo
• Wook Song
• Jaeyun Jung
• Hyoungjoo Ahn
• Parichay Kapoor
• Dongju Chae
• Gichan Jang
• Yongjoo Ahn
• Jihoon Lee
• Hyeonseok Lee
• Mete Ozay
• Hyunil Park
• Jiho Chu
• Yelin Jeong
• Donghak Park
• Hyungjun Seo
• Seungbaek Hong
• Sungsik Kong
• Donghyeon Jeong
• Eunju Yang

Open Source License

The NNtrainer is an open source project released under the terms of the Apache License version 2.0.

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

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}
}