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https://github.com/majianjia/nnom

A higher-level Neural Network library for microcontrollers.
https://github.com/majianjia/nnom

embedded iot machine-learning mcu microcontroller ml neural-networks nnom

Last synced: 4 months ago
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A higher-level Neural Network library for microcontrollers.

Host: GitHub
URL: https://github.com/majianjia/nnom
Owner: majianjia
License: apache-2.0
Created: 2019-01-21T19:38:30.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-03-08T03:36:11.000Z (4 months ago)
Last Synced: 2024-03-08T04:30:51.513Z (4 months ago)
Topics: embedded, iot, machine-learning, mcu, microcontroller, ml, neural-networks, nnom
Language: C
Homepage:
Size: 25.9 MB
Stars: 784
Watchers: 43
Forks: 227
Open Issues: 83
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

Awesome-Embedded - nnom - A higher-level Neural Network library for microcontrollers. (Machine Learning & AI on MCU / USB)
awesome - nnom
edge-ai - Neural Network on Microcontroller (NNoM) - Higher-level layer-based Neural Network library specifically for microcontrollers. Support for CMSIS-NN. (Uncategorized / Uncategorized)
Awesome-Embeded-AI - nnom - A higher-level Neural Network library for microcontrollers. (微控制器 MCU 端 / Awesome-Embedded Repository)
awesome-embedded-software - nnom - Neural Network on Microcontroller (NNoM) is a high-level inference Neural Network library specifically for microcontrollers. (Data processing / AI ML)

README

        
# Neural Network on Microcontroller (NNoM)

[![Build Status](https://travis-ci.com/majianjia/nnom.svg?branch=master)](https://app.travis-ci.com/github/majianjia/nnom)

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

[![DOI](https://zenodo.org/badge/166869630.svg)](https://zenodo.org/badge/latestdoi/166869630)

NNoM is a high-level inference Neural Network library specifically for microcontrollers. 

[[English Manual]](https://majianjia.github.io/nnom/) [[中文简介]](docs/rt-thread_guide.md) 

**Highlights**

- Deploy Keras model to NNoM model with one line of code.

- Support complex structures; Inception, ResNet, DenseNet, Octave Convolution...

- User-friendly interfaces.

- High-performance backend selections.

- Onboard pre-compiling - zero interpreter performance loss at runtime. 

- Onboard evaluation tools; Runtime analysis, Top-k, Confusion matrix... 

The structure of NNoM is shown below:

![](docs/figures/nnom_structure.png)

More detail avaialble in [Development Guide](docs/guide_development.md)

Discussions welcome using [issues](https://github.com/majianjia/nnom/issues). 

Pull request welcome. QQ/TIM group: 763089399.

## Latest Updates - v0.4.x

**Recurrent Layers (RNN) (0.4.1)**

Recurrent layers **(Simple RNN, GRU, LSTM)** are implemented in version 0.4.1. Support `statful` and `return_sequence` options. 

**New Structured Interface (0.4.0)** 

NNoM has provided a new layer interface called **Structured Interface**, all marked with `_s` suffix. which aims to use one C-structure to provided all the configuration for a layer. Different from the Layer API which is human friendly, this structured API are more machine friendly. 

**Per-Channel Quantisation (0.4.0)**

The new structred API supports per-channel quantisation (per-axis) and dilations for **Convolutional layers**. 

**New Scripts (0.4.0)**

From 0.4.0, NNoM will switch to structured interface as default to generate the model header `weights.h`. The scripts corresponding to structured interfaces are `nnom.py` while the Layer Interface corresponding to `nnom_utils.py`.

## Licenses

NNoM is released under Apache License 2.0 since nnom-V0.2.0. 

License and copyright information can be found within the code.

## Why NNoM?

The aims of NNoM is to provide a light-weight, user-friendly and flexible interface for fast deploying on MCU.

Nowadays, neural networks are **wider**, **deeper**, and **denser**.

![](docs/figures/nnom_wdd.png)

>[1] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).

>

>[2] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

>

>[3] Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).

After 2014, the development of Neural Networks are more focus on structure optimising to improve efficiency and performance, which is more important to the small footprint platforms such as MCUs. 

However, the available NN libs for MCU are too low-level which make it sooooo difficult to use with these complex strucures. 

Therefore, we build NNoM to help embedded developers for faster and simpler deploying NN model directly to MCU. 

> NNoM will manage the strucutre, memory and everything else for the developer. All you need to do is feeding your new measurements and getting the results. 

## Documentations

**Guides**

[5 min to NNoM Guide](docs/guide_5_min_to_nnom.md)

[The temporary guide](docs/A_Temporary_Guide_to_NNoM.md)

[Porting and optimising Guide](docs/Porting_and_Optimisation_Guide.md) 

[RT-Thread Guide(Chinese)](https://majianjia.github.io/nnom/rt-thread_guide/)

[RT-Thread-MNIST example (Chinese)](docs/example_mnist_simple_cn.md)

## Performance 

There are many articles compared NNoM with other famous MCU AI tools, such as TensorFlow LiteSTM32Cube.AI .etc. 

**Raphael Zingg etc** from Zurich University of Applied Sciences compare nnom with tflite, cube, and e-Ai in their paper  ["Artificial Intelligence on Microcontrollers"](https://github.com/InES-HPMM/Artificial_Intelligence_on_Microcontrollers/blob/master/Artificial_Intelligence_on_Microcontrollers.pdf) blog https://blog.zhaw.ch/high-performance/2020/05/14/artificial-intelligence-on-microcontrollers/

![performance-comparison-tflite-cubeai-eai](docs/figures/performance-comparison-tflite-cubeai-eai.png)

**Butt Usman Ali** from POLITECNICO DI TORINO, did below comparison in [the thesis: On the deployment of Artificial Neural Networks (ANN) in low 

cost embedded systems](https://webthesis.biblio.polito.it/19692/1/tesi.pdf)

![performance-comparison-tflite-cubeai](docs/figures/performance-comparison-tflite-cubeai.png)

Both articles shows that NNoM is not only comparable with other popular NN framework but with faster inference time and sometime less memory footprint. 

**Note:** These graphs and tables are credited to their authors. Please refer the their original papers for details and copyright. 

## Examples

**Documented examples**

Please check [examples](https://github.com/majianjia/nnom/tree/master/examples) and choose one to start with. 

## Available Operations

[[API Manual]](https://majianjia.github.io/nnom/)

> *Notes: NNoM now supports both HWC and CHW formats. Some operation might not support both format currently. Please check the tables for the current status. *

**Core Layers**

| Layers | Struct API |Layer API|Comments|

| ------ |-------- |------|------|

| Convolution  |conv2d_s()|Conv2D()|Support 1/2D, support dilations (New!)|

| ConvTransposed (New!) |conv2d_trans_s()|Conv2DTrans()|Under Dev. |

| Depthwise Conv |dwconv2d_s()|DW_Conv2D()|Support 1/2D|

| Fully-connected |dense_s()| Dense()| |

| Lambda |lambda_s()| Lambda() |single input / single output anonymous operation| 

| Batch Normalization |N/A| N/A| This layer is merged to the last Conv by the script|

| Flatten|flatten_s()| Flatten()| |

| Reshape (New!)|reshape_s()| N/A| |

| SoftMax|softmax_s()| SoftMax()| Softmax only has layer API| 

| Activation|N/A| Activation()|A layer instance for activation|

| Input/Output |input_s()/output_s()| Input()/Output()| |

| Up Sampling |upsample_s()|UpSample()||

| Zero Padding | zeropadding_s()|ZeroPadding()||

| Cropping |cropping_s() |Cropping()||

**RNN Layers**

| Layers | Status | Struct API |Comments|

| ------ | ------ | ------| ------|

| Recurrent NN Layer(New!) | Alpha | rnn_s()| Layer wrapper of RNN|

| Simple Cell (New!) | Alpha | simple_cell_s()||

| GRU Cell (New!) | Alpha | gru_cell_s()| Gated Recurrent Network |

| LSTM Cell (New!) | Alpha| lstm_s()| Long Short-Term Memory |

**Activations**

Activation can be used by itself as layer, or can be attached to the previous layer as ["actail"](docs/A_Temporary_Guide_to_NNoM.md#addictionlly-activation-apis) to reduce memory cost.

There is no structred API for activation currently, since activation are not usually used as a layer.

| Actrivation | Struct API |Layer API|Activation API|Comments|

| ------ |--|--|--|--|

| ReLU  | N/A |ReLU()|act_relu()||

| Leaky ReLU (New!) | N/A |LeakyReLU()|act_leaky_relu()||

| Adv ReLU(New!) | N/A |N/A|act_adv_relu()|advance ReLU, Slope, max, threshold|

| TanH | N/A |TanH()|act_tanh()||

| Hard TanH (New!)| N/A |TanH()||backend only|

|Sigmoid|N/A| Sigmoid()|act_sigmoid()||

|Hard Sigmoid (New!)|N/A| N/A| N/A|backend only|

**Pooling Layers**

| Pooling | Struct API|Layer API|Comments|

| ------ |--------|----|----|

| Max Pooling |maxpool_s()|MaxPool()||

| Average Pooling |avgpool_s()|AvgPool()||

| Sum Pooling |sumpool_s()|SumPool()||

| Global Max Pooling|global_maxpool_s()|GlobalMaxPool()||

| Global Average Pooling |global_avgpool_s()|GlobalAvgPool()||

| Global Sum Pooling |global_sumpool_s()|GlobalSumPool()|dynamic output shift|

**Matrix Operations Layers**

| Matrix |Struct API |Layer API|Comments|

| ------ |--|--|--|

| Concatenate |concat_s()| Concat()| Concatenate through any axis|

| Multiple  |mult_s()|Mult()||

| Addition  |add_s()|Add()||

| Substraction  |sub_s()|Sub()||

## Dependencies

NNoM now use the local pure C backend implementation by default. Thus, there is no special dependency needed. 

However, You will need to enable `libc` for dynamic memory allocation `malloc(), free(), and memset()`. Or you can port to the equivalent memory method in your system.  

## Optimization

[CMSIS-NN/DSP](https://github.com/ARM-software/CMSIS_5/tree/develop/CMSIS/NN) is an optimized backend for ARM-Cortex-M4/7/33/35P. You can select it for up to 5x performance compared to the default C backend. NNoM will use the equivalent method in CMSIS-NN if the condition met. 

Please check [Porting and optimising Guide](docs/Porting_and_Optimisation_Guide.md) for detail. 

## Known Issues

### The Converter do not support implicitly defined activations

The script currently does not support implicit act:

~~~

x = Dense(32, activation="relu")(x)

~~~

Use the explicit activation instead. 

~~~

x = Dense(32)(x)

x = Relu()(x)

~~~

## Tips - improving accuracy

- Attaching an BatchNormalization after each convolutional layer limit the activation range thus help quantisation. BN add no extra computation in NNoM. 

- Dont train too much epoch. Large epoch number increases extreme number in activation -> lower the quantisation resolution.

- Leave enough data for bottleneck - do not compress data at before the output of a model, infomation will be lost when it is quantised. 

## Contacts

Jianjia Ma

[email protected]

Also find me for field supports. 

## Citation are required in publication

Please contact me using above details if you have any problem. 

Example:

~~~

@software{jianjia_ma_2020_4158710,

  author       = {Jianjia Ma},

  title        = {{A higher-level Neural Network library on Microcontrollers (NNoM)}},

  month        = oct,

  year         = 2020,

  publisher    = {Zenodo},

  version      = {v0.4.2},

  doi          = {10.5281/zenodo.4158710},

  url          = {https://doi.org/10.5281/zenodo.4158710}

}

~~~