README.md

Neural Network on Microcontroller (NNoM)

NNoM is a higher-level layer-based Neural Network library specifically for microcontrollers.

[English Manual] [中文指南]

Highlights

• Deploy Keras model to NNoM model with one line of code.
• User-friendly interfaces.
• Support complex structures; Inception, ResNet, DenseNet, Octave Convolution...
• High-performance backend selections.
• Onboard (MCU) evaluation tools; Runtime analysis, Top-k, Confusion matrix...

The structure of NNoM is shown below:

Discussions welcome using issues. Pull request welcome. QQ/TIM group: 763089399.

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.

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

[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.

NNoM is now working closely with Keras (You can easily learn Keras in 30 seconds!). There is no need to learn TensorFlow/Lite or other libs.

Documentations

Guides

5 min to NNoM Guide

The temporary guide

Porting and optimising Guide

RT-Thread Guide(中文指南)

RT-Thread-MNIST example (中文例子)

Examples

Documented examples

Please check examples and choose one to start with.

MNIST-DenseNet example

Octave Convolution

Keyword Spotting

Available Operations

Layers

Layers Status Layer API Comments Convolution Beta Conv2D() Support 1/2D Depthwise Conv Beta DW_Conv2D() Support 1/2D Fully-connected Beta Dense()

Lambda Alpha Lambda() single input / single output anonymous operation Batch Normalization Beta N/A This layer is merged to the last Conv by the script Input/Output Beta Input()/Output()

Recurrent NN Under Dev. RNN() Under Developpment Simple RNN Under Dev. SimpleCell() Under Developpment Gated Recurrent Network (GRU) Under Dev. GRUCell() Under Developpment Flatten Beta Flatten()

SoftMax Beta SoftMax() Softmax only has layer API Activation Beta Activation() A layer instance for activation

Activations

Activation can be used by itself as layer, or can be attached to the previous layer as "actail" to reduce memory cost.

Actrivation Status Layer API Activation API Comments ReLU Beta ReLU() act_relu()

TanH Beta TanH() act_tanh()

Sigmoid Beta Sigmoid() act_sigmoid()

Pooling Layers

Pooling Status Layer API Comments Max Pooling Beta MaxPool()

Average Pooling Beta AvgPool()

Sum Pooling Beta SumPool()

Global Max Pooling Beta GlobalMaxPool()

Global Average Pooling Beta GlobalAvgPool()

Global Sum Pooling Beta GlobalSumPool() A better alternative to Global average pooling in MCU before Softmax Up Sampling Beta UpSample()

Matrix Operations Layers

Matrix Status Layer API Comments Multiple Beta Mult()

Addition Beta Add()

Substraction Beta Sub()

Dot Under Dev.

Dependencies

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

Optimization

You can select CMSIS-NN/DSP as the backend for about 5x performance with ARM-Cortex-M4/7/33/35P.

Check Porting and optimising Guide for detail.

Contacts

Jianjia Ma

[email protected] or [email protected]

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