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vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers
| Date | Stars |
|---|---|
| 2026-07-31 | 604 |
| 2026-08-01 | 604 |
| 2026-08-06 | 605 |
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# deepC <!-- comment [](https://travis-ci.org/github/ai-techsystems/deepC) --> [](https://badge.fury.io/py/deepC) [](https://pepy.tech/project/deepc) [](LICENSE) [](https://opencollective.com/dnnc) [](https://gitter.im/ai-techsystems/community) The deepC is a **vendor independent deep learning library, compiler and inference framework** designed for small form-factor devices including **μControllers, IoT and Edge devices** ## 🏃♂️ Using deepC Here are few of many ways. 1. Try deepC with [Colab Noteboook](https://colab.research.google.com/drive/1EKgQcMCHr-0OsG9qJ4wXv7J4JFlPY7CK) 1. Install it on Ubuntu, raspbian (or any other debian derivatives) using ```pip install deepC``` 1. Compile onnx model- [read this article](test/compiler/mnist/README.md) or [watch this video](https://youtu.be/BpFs83MU3HM) 1. Use deepC with a [Docker File](Dockerfile) See more examples in [tutorial](tutorials/README.md) dir. ## 📛 what is deepC? deepC library, compiler and inference framework is designed to **enable and perform** deep learning neural networks by focussing on features of small form-factor devices like micro-controllers, eFPGAs, cpus and other embedded devices like [raspberry-pi](https://www.raspberrypi.org/), [odroid](https://www.hardkernel.com/), [arduino](https://www.arduino.cc/), [SparkFun Edge](https://www.sparkfun.com/products/15170), [risc-V](https://www.amazon.com/Seeed-Studio-Sipeed-Maixduino-RISC-V/dp/B07SW9ZWQQ), mobile phones, x86 and arm laptops among others.  deepC also offers ahead of time compiler producing optimized executable based on [LLVM compiler tool chain](https://llvm.org/) specialized for deep neural networks with [ONNX](https://onnx.ai/) as front end. ## 📝 Design Main components of **deepC** have been designed to represent and optimize the common deep learning networks in high level graph IR and to transform the computation graph to minimize memory utilization, optimize data layout and fuse computation patterns for different hardware backends. <img width="600" alt="Architecture" src="https://github.com/ai-techsystems/deepC/blob/master/misc/dnnCompilerArch.jpg"> Read more at [high level design document](docs/highLevelDesign.md) ## 💧 PreRequisites * [ONNX 1.5](https://github.com/onnx/onnx/tree/rel-1.5.0#installation) * [LLVM 8.0](http://releases.llvm.org/8.0.0/docs/GettingStarted.html#getting-started-quickly-a-summary) * [Python 3.6](https://www.python.org/downloads/release/python-360/) * [SWIG 3.0](https://sourceforge.net/projects/swig/files/swig/swig-3.0.12/) ## 💻 Development Build and start modifying deepC locally from source code with following steps ### ⭕ Ubuntu 18.04 Follow the steps to install pre-requisites ```bash sudo apt-get update sudo apt-get install build-essential python3.6-dev python3-pip swig doxygen clang-format clang clang-8 llvm-8 llvm-8-dev protobuf-compiler libprotoc-dev sudo pip3 install numpy==1.15.0 onnx==1.5.0 ``` Once you are done, build deepC ```bash git clone https://github.com/ai-techsystems/deepC.git cd deepC make ``` ### ⭕ Mac OS / Windows 10 Make sure you have the below pre-requisites #### Mac OS: - [Python for Mac](https://www.python.org/downloads/mac-osx/) - [Docker for Mac](https://docs.docker.com/v17.09/docker-for-mac/install/#download-docker-for-mac) #### Windows 10: - [Python for Windows](https://www.python.org/downloads/windows/) - [Docker for Windows](https://docs.docker.com/v17.09/docker-for-windows/install/#download-docker-for-windows) Once you are done, build deepC inside docker c
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b76216753bad1497, llm:README and topics: describes a vendor-independent TinyML deep learning library, compiler and inference framework for microcontrollers, IoT and edge devices; topics include tinyml, inference-framework, onnx, edge-devices, microcontrollers, deep-learning, machine-learning.
matched fp:b76216753bad1497, llm:README and topics: describes a vendor-independent TinyML deep learning library, compiler and inference framework for microcontrollers, IoT and edge devices; topics include tinyml, inference-framework, onnx, edge-devices, microcontrollers, deep-learning, machine-learning.
matched fp:b76216753bad1497, llm:README and topics: describes a vendor-independent TinyML deep learning library, compiler and inference framework for microcontrollers, IoT and edge devices; topics include tinyml, inference-framework, onnx, edge-devices, microcontrollers, deep-learning, machine-learning.
matched fp:b76216753bad1497, llm:README and topics: describes a vendor-independent TinyML deep learning library, compiler and inference framework for microcontrollers, IoT and edge devices; topics include tinyml, inference-framework, onnx, edge-devices, microcontrollers, deep-learning, machine-learning.