python神经网络框架_NNoM是一个专门为了神经网络在 MCU 上运行的框架
Neural Network on Microcontroller (NNoM)
NNoM is a high-level linference Neural Network library specifically for microcontrollers.
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:
More detail avaialble in Development Guide
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
Examples
Documented examples
Please check examples and choose one to start with.
Available Operations
*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
HWC
CHW
Layer API
Comments
Convolution
✓
✓
Conv2D()
Support 1/2D
Depthwise Conv
✓
✓
DW_Conv2D()
Support 1/2D
Fully-connected
✓
✓
Dense()
Lambda
✓
✓
Lambda()
single input / single output anonymous operation
Batch Normalization
✓
✓
N/A
This layer is merged to the last Conv by the script
Flatten
✓
✓
Flatten()
SoftMax
✓
✓
SoftMax()
Softmax only has layer API
Activation
✓
✓
Activation()
A layer instance for activation
Input/Output
✓
✓
Input()/Output()
Up Sampling
✓
✓
UpSample()
Zero Padding
✓
✓
ZeroPadding()
Cropping
✓
✓
Cropping()
RNN Layers
Layers
Status
Layer API
Comments
Recurrent NN
Under Dev.
RNN()
Under Developpment
Simple RNN
Under Dev.
SimpleCell()
Under Developpment
Gated Recurrent Network (GRU)
Under Dev.
GRUCell()
Under Developpment
Activations
Activation can be used by itself as layer, or can be attached to the previous layer as "actail" to reduce memory cost.
Actrivation
HWC
CHW
Layer API
Activation API
Comments
ReLU
✓
✓
ReLU()
act_relu()
TanH
✓
✓
TanH()
act_tanh()
Sigmoid
✓
✓
Sigmoid()
act_sigmoid()
Pooling Layers
Pooling
HWC
CHW
Layer API
Comments
Max Pooling
✓
✓
MaxPool()
Average Pooling
✓
✓
AvgPool()
Sum Pooling
✓
✓
SumPool()
Global Max Pooling
✓
✓
GlobalMaxPool()
Global Average Pooling
✓
✓
GlobalAvgPool()
Global Sum Pooling
✓
✓
GlobalSumPool()
A better alternative to Global average pooling in MCU before Softmax
Matrix Operations Layers
Matrix
HWC
CHW
Layer API
Comments
Concatenate
✓
✓
Concat()
Concatenate through any axis
Multiple
✓
✓
Mult()
Addition
✓
✓
Add()
Substraction
✓
✓
Sub()
Dependencies
NNoM now use the local pure C backend implementation by default. Thus, there is no special dependency needed.
Optimization
CMSIS-NN/DSP 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.
Known Issues
Converter do not support implicitly defined activations
The script currently does not support implicit act:
Dense(32, activation="relu")
Use the explicit activation instead.
Dense(32)
Relu()
Contacts
Jianjia Ma
Citation Required
Please contact us using above details.