Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A higher-level Neural Network library for microcontrollers.
| Date | Stars |
|---|---|
| 2026-07-31 | 1162 |
| 2026-08-04 | 1163 |
| 2026-08-05 | 1163 |
| 2026-08-06 | 1163 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Neural Network on Microcontroller (NNoM) [](https://app.travis-ci.com/github/majianjia/nnom) [](https://opensource.org/licenses/Apache-2.0) [](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:  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**.  >[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. ## Installing NNoM can be installed as a Python package ``` pip install git+https://github.com/majianjia/nnom@master ``` NNoM requires [Tensorflow](https://www.tensorflow.org/) version `<= 2.14`. There are multiple options for how to install this, see the TensorFl
Excerpt of 11,129 characters
Read on GitHub
614
Jon Nordby · @soundsensing · Norway
17
12
5
2
1
1
1
1
1
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:02d705b691d19071, llm:Repository description and README: 'Neural Network on Microcontroller (NNoM)', 'high-level inference Neural Network library specifically for microcontrollers', topics: embedded, iot, machine-learning, mcu, microcontroller, ml, neural-networks, nnom.
matched fp:02d705b691d19071, llm:Repository description and README: 'Neural Network on Microcontroller (NNoM)', 'high-level inference Neural Network library specifically for microcontrollers', topics: embedded, iot, machine-learning, mcu, microcontroller, ml, neural-networks, nnom.